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Nowadays, most car accidents occur due to driver fatigue, distraction, and sleepiness, and generally due to human error. Therefore, the importance of having cars with self-driving systems or advanced driver assistant systems has always been felt; because with the help of these systems, road traffic is managed by minimizing human intervention. One of the most essential features of such intelligent systems is the ability to recognize traffic signs. The paper introduces a novel framework for recognizing Iranian traffic signs, utilizing transfer learning and convolutional neural networks. Initially, a convolutional neural network is trained with the GTSRB dataset. Subsequently, this trained network is integrated as a feature-extracting block within a new network tailored for classifying Iranian traffic signs. The proposed model achieved an accuracy of 97.00% on the PTSD dataset. This framework represents the first instance of utilizing transfer learning for the detection and classification of Persian traffic signs. Additionally, this article pioneers the classification of Iranian traffic signs into 43 distinct classes, facilitated by the transfer learning method and the PTSD dataset. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems, real-time detection performance and accuracy are essential metrics. This paper proposes a lightweight real-time detection model, RT-DETRmg, to address the challenges of false and missed detections of small traffic signs and to improve the algorithm's real-time performance. RT-DETRmg enhances the multi-scale feature extraction capability of the RT-DETR backbone network by incorporating a Multiple Scale Sequence Fusion module, which effectively integrates global and local semantic information from different scales of images. Additionally, a cascaded group attention module is utilized within an efficient hybrid encoder to reduce computational complexity, thereby enhancing real-time performance. To further optimize small object detection, a small receptive field feature layer is introduced, while a large receptive field feature layer is removed. Experimental results on the TT100K and GTSDB datasets demonstrate the superiority of RT-DETRmg over existing models. On the TT100K dataset, RT-DETRmg achieves a 2.0% improvement in mean average precision and a 6.6% increase in frames per second compared to the baseline RT-DETR model, while reducing model parameters and computational complexity. On the GTSDB dataset, RT-DETRmg further demonstrates its strong generalization ability, achieving a 2.2% improvement in the F1 score and a 1.7% increase in mean average precision compared to the baseline network. These findings highlight the effectiveness of RT-DETRmg in enhancing both detection accuracy and real-time performance of small traffic signs in diverse scenarios. [ABSTRACT FROM AUTHOR]

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method. [ABSTRACT FROM AUTHOR]

Traffic sign detection plays an essential role in the technology of self-driving vehicles. Recently, deep learning methods have significantly advanced the field of traffic sign recognition. Nevertheless, faced with increasingly complex traffic scenarios, practical applications of traffic sign detection still encounter challenges, including false detections, missed detections, and reduced accuracy. To tackle these challenges, we introduce an enhanced algorithm for traffic sign detection built on the YOLOv8 model, aimed at improving performance and accuracy. Firstly, a Multi-Scale Convolutional Attention (MSCA) module is embedded into the backbone architecture to improve the model's feature extraction capabilities at multiple scales, enhancing its focus on target areas. Furthermore, a small object detection layer is added during the detection phase, effectively reducing the false positive and missed detection rates for small objects. Finally, we present the Inner-WIoU loss function for bounding boxes, which integrates a dynamic non-monotonic focusing mechanism with auxiliary boxes. This boosts the model's capability to identify objects and enhances overall detection performance. The findings from the experiments demonstrate that the enhanced algorithm obtains an mAP0.5 value of 83.8% on the TT100K dataset, indicating a 7.8% increase compared to the baseline YOLOv8 algorithm. When compared to existing algorithms, the proposed method demonstrates competitive performance. [ABSTRACT FROM AUTHOR]

Traffic sign detection is a crucial aspect of advanced driver assistance systems (ADAS) for academic research and the automotive industry. seeing that accurate and timely detection of traffic signs (TS) is essential for ensuring the safety of driving. However, TS detection methods encounter challenges like slow detection speed and a lack of robustness in complex environments. This paper suggests addressing these limitations by proposing the use of the you only look one version 7 (YOLOv7) network to detect and recognize TS in road scenes. Furthermore, the k-means++ algorithm is used to acquire anchor boxes. Additionally, a tiny version of YOLOv7 is used to take advantage of its real-time and low model size, which are required for real-time hardware implementation. So, we conducted an experiment using our proprietary Morocco dataset. According to the experimental results, YOLOv7 achieves 85% in terms of mean average precision (mAP) at 0.5 for all classes. And YOLOv7-tiny obtains 90% in the same term. Afterward, a recognition system for the prohibitive class using the convolutional neural network (CNN) is trained and integrated inside the YOLOv7 algorithm; its model achieves an accuracy of 99%, which leads to a good specification of the prohibitive sign meaning. [ABSTRACT FROM AUTHOR]

Automatic driving requires real-time consideration for traffic sign target detection algorithms while ensuring the accuracy. However, the current one-stage target detection algorithm mainly used for real-time detection is not focused on the characteristics of traffic signs, and the relevant research is insufficient. Aiming at this problem and ensure the accuracy of light-weight network in traffic sign detection task, an improved lightweight traffic sign recognition algorithm based on YOLOv4-Tiny was proposed, with improved backbone feature extraction and detection head using CBAM attention mechanism and depth-wise separable convolution, known as CDYOLO. Based on CDYOLO, we further proposed CDYOLO-SP, which can perform well in complex multi-category detection tasks. In terms of training methods, we adopt the transfer learning mode of "CCTSDB + TT100K" to improve performance. Compared with the original YOLOv4-Tiny, the improved algorithm has achieved better results. In the CCTSDB three-classification task, the mAP of CDYOLO improved by 6.52% and FPS maintained at about 82.5 FPS. The model size is only 4.1 MB. In the TT100K complex multi-classification task, the mAP of CDYOLO-SP improved by 48.59% and FPS maintained at about 60.2 FPS, and the model size is only 10.0 MB. Furthermore, the experiments show that compared with different CNN-based methods our methods outperforms them significantly. In summary, the improved model can meet the accuracy and real-time requirements of traffic sign detection and can be deployed on low-performance devices. [ABSTRACT FROM AUTHOR]

Traffic sign detection is essentially important for intelligent driving. Existing detection algorithms typically incorporate self-attention mechanisms to model the dependencies among image elements, such as patches or pixels. When using a patch as a token, the positional information within the patch could be lost. When using a pixel as a token, an increase in the number of tokens can lead to a significant increase in computational complexity. To balance these two extreme situations, a pixel only needs to focus on pixels from the surrounding area. Therefore, we propose a local attention module termed Pixel-wise Spatial Feature Enhancement (PSFE), which uses pixels as tokens to enhance the spatial information of feature maps, and each pixel’s self-attention only acts on a local region to reduce computational complexity. Furthermore, we design a Bidirectional Res2Net (BR) module that generates multiple feature maps with different channel numbers from an input feature map, and then restores them to one feature map with the original input size through bidirectional fusion, greatly enriching the receptive field information contained in the feature map. We conducted experiments on the GTSDB, TT100K, and CCTSDB 2021 datasets to comprehensively evaluate our method, and the experimental results showed that our method has superior performance. [ABSTRACT FROM AUTHOR]

Traffic signs have great importance in driving safety. For the recently emerging autonomous vehicles, that can automatically detect and recognize all road inventories such as traffic signs. Firstly, in this study, a method based on a mobile mapping system (MMS) is proposed for the detection of traffic signs to establish a Turkish traffic sign dataset. Obtaining images from real traffic scenes using the MMS method enhances the reliability of the model. It is an easy method to be applied to real life in terms of both cost and suitability for mobile and autonomous systems. In this frame, YOLOv4-Tiny, one of the object detection algorithms, that is considered to be more suitable for mobile vehicles, is used to detect and recognize traffic signs. This algorithm is low operation cost and more suitable for embedded devices due to its simple neural network structure compared to other algorithms. It is also a better option for real-time detection than other approaches. For the training of the model in the suggested method, a dataset consisting partly of images taken with MMS based on realistic field measurement and partly of images obtained from open data sets was used. This training resulted in the mean average precision (mAP) value being obtained as 98.1%. The trained model was first tested on existing images and then tested in real time in a laboratory environment using a simple fixed web camera. The test results show that the suggested method can improve driving safety by detecting traffic signs quickly and accurately, especially for autonomous vehicles. Therefore, the proposed method is considered suitable for use in autonomous vehicles. [ABSTRACT FROM AUTHOR]

Traffic sign assisted recognition technology is becoming more and more important in the automatic driving system. Due to the different bearing capacity of different hardware equipment, it has become one of the research directions of various units and enterprises to make the model lighter while the performance is unchanged or better. In order to make the model lighter and improve the model recognition effect and detection speed, a lightweight traffic sign detection method based on SWimAM (a simple, parameter-free attention module add weight part for convolutional neural networks) designed YOLOv5 (you only look once version 5) was proposed. This method added iterable learning weights based on SimAM mechanism and changes the calculation method of internal weights. SWimAM module was proposed and C3 layer of YOLOv5 backbone structure was replaced with this module. The head part was fused with SE (squeeze-and-excitation networks) attention mechanism and the loss function was replaced by SIoU (soft intersection over union) to enhance the detection accuracy of the model and reduce the instability of the gradient. A data enhancement method of filtering Mosaic images was proposed to solve the non-uniformity of partial labels. On the final TT100K traffic sign data set, the recognition mean average precision of the improved YOLOv5s network increase by 2. 5%, calculation speed increased by 7. 33%, computational complexity decreased by 3. 07%, and the number of references decreased by 9. 27% . The mean average precision of Chinese traffic sign detection data set CCTSDB and German traffic sign detection data set GTSDB reached 94. 9% and 94. 7%, respectively, which verified that the model has good generalization. [ABSTRACT FROM AUTHOR]

To address the problems of misdetection, omission, and low accuracy in traffic sign detection and recognition, a novel method called RBL-YOLOv8 is presented by improving YOLOv8. In the feature extraction network, the RepNCSPELAN module is used to replace the C2f module to improve the feature extraction capability and reduce the number of parameters. In the feature fusion network, fusion of largescale feature layers is added, while weighted feature fusion is used to create cross-layer connections between shallow and deep features to improve the utilisation of shallow features for better detection of small targets. A lightweight detection head is proposed to reduce the number of parameters and computational complexity of the model, while improving the localization and classification ability of the detection head. The MPDIoU loss function is used to replace CIOU, which can better accelerate the bounding box regression. The improved model is conducted experiments on the CCTSDB and TT100K datasets and compared with other algorithms, the results validate its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Detecting traffic sign small targets accurately and in real time in natural environment is of great significance for automatic driving and intelligent transportation, however, the existing algorithms are difficult to balance the speed and accuracy. Based on the YOLOv7-tiny algorithm, a real-time traffic sign small target detection algorithm that improves YOLOv7-tiny was proposed, namely YOLO-T algorithm. CondConv (conditional parameterized convolution) structure was used to enhance the feature extraction capability of the backbone network. To enhance the localization accuracy of small targets and ensure the detection speed, the TinyFPN feature fusion network structure and ELAN-P network aggregation layer were designed. To verify the effectiveness of the YOLO-T algorithm, ablation experiments and comparison experiments were done on the TT100K dataset. The experimental results show that YOLO-T improves mAP (mean average precision) by 16. 8% over the YOLOv7-tiny algorithm with the same training samples and training device parameters, and the detection time of a single image is only 10. 2 ms. Therefore, the proposed YOLO-T algorithm is able to balance the speed and accuracy of the detection of the small targets of traffic signs. [ABSTRACT FROM AUTHOR]

Traffic sign detection plays an integral role in intelligent driving systems. It was found that in real driving scenarios, traffic signs were easily obscured by haze leading to traffic sign detection inaccuracy in assisted driving systems. Therefore, we designed a traffic sign detection model for hazy weather that can effectively help drivers to recognize road signs and reduce the incidence of traffic accidents. A high-precision traffic sign detection network has been designed to address the problem of decreased model recognition performance caused by external factors such as small size of traffic signs and haze obstruction in real-world scenarios. First, the default YOLOv8 was found to have low model detection accuracy in hazy weather occlusion conditions through experimental studies. Therefore, a deeper lightweight and efficient multi-branch CSP (Cross Stage Partial) module was introduced. Second, a dual path feature fusion network was designed to address the problem of insufficient feature fusion due to the small size of traffic signs. Finally, in order to be able to better simulate the real haze weather scene, we added fog to the raw data to enrich the data samples. This was verified through experiments on a public Chinese traffic sign detection dataset after fogging treatment, compared to the default YOLOv8 model. The improved DPF-YOLOv8 algorithm achieved 2.1% and 2.2% improvement in mAP@0.5 and mAP@0.5:0.95 performance metrics to 65.0% and 47.4%, respectively. [ABSTRACT FROM AUTHOR]

With the continuous development of autonomous driving, traffic sign detection, as an essential subtask, has witnessed constant updates in corresponding technologies. Currently, traffic sign detection primarily confronts challenges such as the small size of detection targets and the complexity of detection scenarios. This paper focuses on detecting small traffic signs in low-light scenarios. To address these issues, this paper proposes a traffic sign detection method that integrates low-light image enhancement with small target detection, namely, LLE-STD. This method comprises two stages: low-light image enhancement and small target detection. Based on classic baseline models, we tailor the model structures by considering the requirements of lightweight traffic sign detection models and their adaptability to varying image qualities. The two stages are then coupled to form an end-to-end processing procedure. During experiments, we validate the performance of low-light image enhancement small target detection, and adaptability to images of different qualities using the public datasets GTSDB, TT-100K, and GLARE. Compared to classic models, LLE-STD demonstrates significant advantages. For example, the mAP results tested on the GLARE dataset show that LLE-STD outperforms RetinaNet by approximately 15%. This research can facilitate the practical application of deep learning-based intelligent methods in the field of autonomous driving. [ABSTRACT FROM AUTHOR]

Traffic sign detection is an important research content of Autonomous Driving Systems, which can effectively guide vehicles or driver to make correct decisions and reduce traffic accidents. The existing real-time traffic sign detectors have low detection accuracy for small objects. Therefore, we propose a novel real-time traffic sign detector based on YOLOv5 for the accurate detection of small objects. Specifically, we propose a new Multi-layer Interactive Residual Feature Fusion Network (MIRFFN) in the neck, which can effectively combine the position information of the low-layer feature maps with the semantic information of the high-layer feature maps, and refine the features by fusing different layers of feature maps. Then, we design a Residual Information Fusion (RIF) module for MIRFFN to fuse feature maps from different layers. The RIF module is composed of three residual blocks to refine spatial position information. Inspired by Bi-level Routing Attention effectively extracting small objects, we design a Stage Routing Attention (SRA) module in the backbone. The SRA modules can search the most relevant regions and enhance attention to small traffic signs in high-layer feature maps. We conduct experiments on GTSDB, TT100K, and CCTSDB2021, and achieve mAP of 96.2%, 72.7%, and 90.8%, respectively. Our method achieves 48.91 FPS on the GTSDB dataset. The experimental results show that our method can accurately perform real-time traffic sign detection. [ABSTRACT FROM AUTHOR]

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems rely heavily on accurate traffic sign detection (TSD) to enhance road safety and traffic management. Various methods have been explored in the literature for this purpose, with deep learning methods consistently demonstrating superior accuracy. However, existing research highlights the persistent challenge of achieving high accuracy rates while maintaining non-destructive and real-time requirements. In this study, we propose a deep learning model based on the YOLOv8 architecture to address this challenge. The model is trained and evaluated using a custom dataset, and extensive experiments and performance analysis demonstrate its ability to achieve precise results, thus offering a promising solution to the current research challenge in deep learning-based TSD. [ABSTRACT FROM AUTHOR]

The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. The paper introduces an innovative solution in the form of a convolutional neural network based on the YOLOv8 architecture, underpinned by a custom dataset representing real-world traffic sign images and rigorous training. Through extensive experimentation, the proposed model not only proves highly effective but consistently achieves remarkable accuracy rates, successfully meeting the stringent real-time processing requirements crucial for self-driving systems, thus advancing the safety and efficiency of autonomous vehicles and shaping the future of transportation. [ABSTRACT FROM AUTHOR]

Traffic mark detection and identification play a key character in the development of driverless and intelligent transportation systems, offering significant assistance in ensuring the safety of people's daily travels. However, the detection effect of traffic signs is affected by many target categories, small targets, and low recognition accuracy, making traffic sign detection more challenging than target detection in general scenarios. In this paper, an improved YOLOv7 network (YOLOv7-COORD) is entered. Foremost, increase CBAM attention module at the connection between backbone and neck network of YOLOv7 to enhance the expression ability of neural networks through the attention mechanism, emphasizing important features and ignoring minor features to enhance the efficiency and precision of the network. Secondly, By adding CoordConv before the upsampling of the neck and before the detection head output, the network can better feel the location message in the characteristic map. Finally, a detection head generated by the low-level, high-resolution characteristic map is added to enhance the recognition accuracy of small target object. The abundance of experimental data demonstrates that the impression of the improved YOLOv7-COORD model is superior to that of the original YOLOv7 model, and the average accuracy of (mAP@0.5) on TT100K datasets is 3.2% higher than that of YOLOv7, reaching 85.4%. In summary, the improved YOLOv7-COORD model can better detect targets in traffic sign images. [ABSTRACT FROM AUTHOR]

Traffic sign detection is a crucial element of advanced driver assistance systems (ADAS) for environmental perception. However, challenges persist in the detection of small-scale targets and susceptibility to adverse weather, varying light conditions, and occlusions. To address this issue, a novel traffic sign detection algorithm, GRFS-YOLOv8, is proposed. GRFS-YOLOv8 introduces an enhanced greater receptive field-SPPF (GRF-SPPF) module to replace the original SPPF module, enabling the capture of richer multiscale features from image feature maps. Additionally, a new SPAnet architecture is designed by introducing two "shortcut" paths and additional smaller target detection layers to enhance path aggregation capabilities. This architecture propagates complete semantic information, alleviating the reduction in resolution for small targets and enhancing the model's capability to detect them. Finally, by employing GhostConv and C2fGhost to replace multiple CBS and C2f modules in Backbone and Neck, cost-effective linear operations are utilized to obtain more feature maps, thus reducing the computational cost of the model. Experimental validation across multiple datasets demonstrates the efficacy and adaptability of GRFS-YOLOv8, achieving an 80.3% mAP and 72.4% Recall in CCTSDB 2021, a 71.2% mAP and 95% Recall in TT100K, and a 94.0% mAP and 96.0% Recall in GTSDB, surpassing mainstream detectors and comparative methods. [ABSTRACT FROM AUTHOR]

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A road intersection is an area where more than two roads in different directions connect. It is a point of transition where the driver navigates and makes the decision, making it an area with a high risk for traffic accidents. Road intersection detection is identifying and analyzing road intersections in real time using various technologies and algorithms. It is an essential part of intelligent transportation systems and autonomous driving. Road intersection detection helps the driver to identify the road intersection early to make good driving decisions and avoid accidents. Despite its high importance, only a few research is found regarding this topic. Existing research mainly focuses on detecting and classifying traffic signs, vehicles, and pedestrians. In this research, we propose an algorithm to detect road intersections using an image from the front-facing camera installed on the car as an input. We use traffic sign detection to detect seven types of traffic signs having a high probability of intersection nearby and combine it with our novel road intersection detection algorithm to detect the location of the road intersection. Our road inter-section detection algorithm leverages the relationship between the area of the traffic signs and the location of the intersection. Our proposed method gives promising results from the experiments and can detect road intersections from further distances. Our method is also able to perform detection in real time. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird's-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a mAP@0.5 of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Autonomous driving technology has entered a period of rapid development, and traffic sign detection is one of the important tasks. Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced, and traffic sign targets are small and have unclear features, which makes detection more difficult. Therefore, we propose a Hybrid Feature Fusion Traffic Sign detection algorithm based on YOLOv7 (HFFT-YOLO). First, a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales; Secondly, the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion method. Design reuse paths at the end of the neck, paying particular attention to cross-scale fusion of high-level features. In addition, we found the appropriate channel width through a lot of experiments and reduced the superfluous parameters. In terms of training, a new regression loss CMPDIoU is proposed, which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different, but also enables the penalty term to dynamically change at different scales. Finally, our proposed improved method shows excellent results on the TT100K dataset. Compared with the baseline model, without increasing the number of parameters and computational complexity, and increased by 2.2% and 2.7%, respectively, reaching 92.9% and 58.1%. [ABSTRACT FROM AUTHOR]

With the rise of deep learning technology, significant progress has been made in object detection. Traffic sign detection is a research hotspot for object detection tasks. However, due to small size of traffic signs, there is room for further improvement in the comprehensive performance of the existing technology. In this paper, we propose a lightweight network based on yolov5s to achieve real-time localization and classification of small traffic signs. First, we improve the bottleneck transformers with 3 convolution (Bot3) module to enhance the backbone network's ability to extract features from small targets, improving the accuracy while reducing the number of parameters and giga floating-point operations per second (GFLOPs). Second, we introduce ghost convolution (GhostConv) to obtain redundant feature maps with cheap operations to further improve the model's efficiency. Finally, we use soft non-maximum suppression (Soft-NMS) in the detection phase to improve the model accuracy again without additional computational overhead for training. According to the tests on the Tsinghua-Tencent 100 K (TT100K) dataset, the proposed method outperforms the original YOLOv5s in small traffic sign detection, with an increase of 8.7% in m A P 50 , a reduction of 22.5% in parameter count, and a 17.2% reduction in computational complexity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Propose an optimized and improved traffic sign detection model based on YOLOv8n, addressing the issues of low accuracy and inaccurate detection, especially in adverse weather conditions, observed in current traditional network models. We leverage existing techniques, including the BoTNet (Bottleneck Transformers for Visual Recognition) module to enhance image classification capabilities, the ODConv (Omni-dimensional Dynamic Convolution) module to supplement attention for improved accuracy, and the LSKA (Large Separable Kernel Attention) module to reduce memory and computational complexity while enhancing small object detection capabilities. Additionally, we employ the WIoU (Wise Intersection over Union) loss function to enhance the model’s generalization performance. Without additional preprocessing to simulate adverse weather conditions, our results on the TT100K dataset, including mAP50, mAP95 (mAP is ’mean Average Precision’), and F1, relative to the original YOLOv8n model, show improvements of 3%, 4%, and 2.5% in misty conditions and 1%, 2.6%, and 1.7% in dark conditions, respectively. On the GTSDB dataset, in misty conditions, the improvements are 5%, 4.2%, and 2.3%, and in dark conditions, the improvements are 3%, 6.6%, and 6%. When 30% of the training set is augmented with fog, the detection performance of the improved model is comparable to, or even exceeds, that of the YOLOv8n model trained with the entire fog-augmented training set. This comprehensive result highlights the significant superiority of our model over the comparative model, demonstrating its practical applicability.

The challenge of evaluating deep learning-based object detection models in complex traffic scenarios, characterized by changing weather and lighting conditions, is addressed in this study. Real-world testing proves time and cost-intensive, leading to the proposal of a Video Frame Feeding (VFF) approach as a solution. The proposed Video Frame Feeding approach acts as a bridge between object detection models and simulated environments, enabling the generation of realistic scenarios. Leveraging the CarMaker (CM) tool to simulate realistic scenarios, the framework utilizes a virtual camera to capture the simulated environment and feed video frames to an object identification model. The VFF algorithm, with automated validation using simulated ground truth data, enhances detection accuracy to over 95% at 30 frames per second within 130 meters. Employing the You Only Look Once (YOLO) version 4 and the German Traffic Sign Recognition Benchmark dataset, the study assesses a traffic signboard identification model across various climatic conditions. Notably, the VFF algorithm improves accuracy by 2% to 5% in challenging scenarios like foggy days and nights. This innovative approach not only identifies object detection issues efficiently but also offers a versatile solution applicable to any object detection model, promising improved dataset quality and robustness for enhanced model performance.

Traffic sign recognition plays a crucial role in enhancing the safety and efficiency of traffic systems. However, in snowy conditions, traffic signs are often obscured by particles, leading to a severe decrease in detection accuracy. To address this challenge, we propose an improved YOLOv8-based model for traffic sign recognition. Initially, we introduce a Multi-Scale Group Convolution (MSGC) module to replace the C2f module in the YOLOv8 backbone. Data indicate that MSGC enhances detection accuracy while maintaining model lightweightness. Subsequently, to improve the recognition ability for small targets, we introduce an enhanced small target detection layer, which enhances small target detection accuracy while reducing parameters. In addition, we replaced the original BCE loss with the improved EfficientSlide loss to improve the sample imbalance problem. Finally, we integrate Deformable Attention into the model to improve the detection efficiency and performance of complex targets. The resulting fused model, named MSGC-YOLOv8, is evaluated on an enhanced dataset of snow-covered traffic signs. Experimental results show that the MSGC-YOLOv8 model is used for snow road traffic sign recognition. Compared with the YOLOv8n model mAP@0.5:0.95, mAP@0.5:0.95 is increased by 17.7% and 18.1%, respectively, greatly improving the detection accuracy. Compared with the YOLOv8s model, while the parameters are reduced by 59.6%, mAP@0.5 only loses 1.5%. Considering all aspects of the data, our proposed model shows high detection efficiency and accuracy under snowy conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately detecting small traffic signs in complex and changing environments is challenging. Although the detection accuracy of traffic signs has been improved with the development of deep learning, it is not effective in detecting small traffic signs. We propose soft-channel spatial attention to solve this problem. Specifically, redundant information is suppressed while maintaining the high and low dimensional feature information, and the traffic sign region is enhanced. In addition, a pyramid scale-aware module is designed to dynamically generate different size receptive fields according to the regional context of the input image and construct a feature-rich feature pyramid to detect the diversity of object scales. Our approach is tested on public transportation sign detection datasets. A large number of experimental results have proved the superiority and effectiveness of the proposed approach compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology. [ABSTRACT FROM AUTHOR]

Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model. [ABSTRACT FROM AUTHOR]

Traffic sign detection technology plays an important role in driver assistance systems and automated driving systems. This paper proposes DeployEase-YOLO, a real-time high-precision detection scheme based on an adaptive scaling channel pruning strategy, to facilitate the deployment of detectors on edge devices. More specifically, based on the characteristics of small traffic signs and complex background, this paper first of all adds a small target detection layer to the basic architecture of YOLOv5 in order to improve the detection accuracy of small traffic signs.Then, when capturing specific scenes with large fields of view, higher resolution and richer pixel information are preserved instead of directly scaling the image size. Finally, the network structure is pruned and compressed using an adaptive scaling channel pruning strategy, and the pruned network is subjected to a secondary sparse pruning operation. The number of parameters and computations is greatly reduced without increasing the depth of the network structure or the influence of the input image size, thus compressing the model to the minimum within the compressible range. Experimental results show that the model trained by Experimental results show that the model trained by DeployEase-YOLO achieves higher accuracy and a smaller size on TT100k, a challenging traffic sign detection dataset. Compared to existing methods, DeployEase-YOLO achieves an average accuracy of 93.3%, representing a 1.3% improvement over the state-of-the-art YOLOv7 network, while reducing the number of parameters and computations to 41.69% and 59.98% of the original, respectively, with a compressed volume of 53.22% of the previous one. This proves that the DeployEase-YOLO has a great deal of potential for use in the area of small traffic sign detection. The algorithm outperforms existing methods in terms of accuracy and speed, and has the advantage of a compressed network structure that facilitates deployment of the model on resource-limited devices. [ABSTRACT FROM AUTHOR]

The challenge of evaluating deep learning-based object detection models in complex traffic scenarios, characterized by changing weather and lighting conditions, is addressed in this study. Real-world testing proves time and cost-intensive, leading to the proposal of a Video Frame Feeding (VFF) approach as a solution. The proposed Video Frame Feeding approach acts as a bridge between object detection models and simulated environments, enabling the generation of realistic scenarios. Leveraging the CarMaker (CM) tool to simulate realistic scenarios, the framework utilizes a virtual camera to capture the simulated environment and feed video frames to an object identification model. The VFF algorithm, with automated validation using simulated ground truth data, enhances detection accuracy to over 95% at 30 frames per second within 130 meters. Employing the You Only Look Once (YOLO) version 4 and the German Traffic Sign Recognition Benchmark dataset, the study assesses a traffic signboard identification model across various climatic conditions. Notably, the VFF algorithm improves accuracy by 2% to 5% in challenging scenarios like foggy days and nights. This innovative approach not only identifies object detection issues efficiently but also offers a versatile solution applicable to any object detection model, promising improved dataset quality and robustness for enhanced model performance. [ABSTRACT FROM AUTHOR]

Traffic signs detection is a critical function of automatic driving and assisted driving is a significant part of Cooperative Intelligent Transport Systems (CITS). The drivers can obtain the data attained via automated traffic sign detection to improve the comfort and security of motor vehicle driving and regulate the behaviors of drivers. Recently, deep learning (DL) has been utilized in the fields of traffic sign detection and achieve better results. But there are two major problems in traffic sign recognition to be immediately resolved. Some false sign is always detected due to the interference caused by bad weather, and illumination variation. Some traffic signs of smaller size are increasingly complex to identify than larger size hence the smaller traffic signs go unnoticed. The objective is to achieve the accuracy and robustness of traffic sign detection for detecting smaller traffic signs in a complex environment. Thus, the study presents a Golden Jackal Optimization with Neutrosophic Rule-Based Classification System (GJO-NRCS) technique for Enhanced Traffic Sign Detection. The GJO-NRCS technique aims to detect the presence of distinct types of traffic signs. In the GJO-NRCS technique, DenseNet201 model is exploited for feature extraction process and the GJO algorithm is used for hyperparameter tuning process. For final recognition of traffic signals, the GJO-NRCS technique applies NRCS technique. The simulation values of the GJO-NRCS method can be examined using benchmark dataset. The experimental results inferred that the GJO-NRCS method reaches high efficiency than other techniques. [ABSTRACT FROM AUTHOR]

The ability to recognize traffic signs is a critical skill for safe driving, as traffic signs provide drivers with essential information about the road conditions, potential hazards, speed limits, and other important details that can impact their driving. By recognizing and understanding traffic signs, drivers can react quickly and appropriately to different situations on the road, which helps prevent accidents and ensures the safety of all road users. As part of the Robotaxi-Full Scale Autonomous Vehicle Competition, a project was undertaken to develop a traffic sign recognition system using YOLOv8, a state-of-the-art deep learning model that can detect and classify objects in real-time. The project team trained YOLOv8 on a dataset of traffic sign images to create a model that could accurately recognize and classify different types of traffic signs. This traffic sign recognition system has the potential to significantly improve road safety by helping autonomous vehicles and human drivers to better understand their surroundings and react appropriately to changing road conditions. The system can assist drivers by providing real-time alerts and warnings about potential hazards, speed limits, and other important information. Furthermore, this project demonstrates the power and potential of deep learning and artificial intelligence in improving transportation safety and efficiency. As AI technology continues to advance, we can expect to see more innovative applications in the automotive industry that will help improve the driving experience and make our roads safer for everyone. [ABSTRACT FROM AUTHOR]

Accurate and fast detection of traffic sign information is vital for autonomous driving systems. However, the YOLOv5 algorithm faces challenges with low accuracy and slow detection when it is used for traffic sign detection. To address these shortcomings, this paper introduces an accurate and fast traffic sign detection algorithm–YOLOv5-Efficient Vision TransFormer(EfficientViT)). The algorithm focuses on improving both the accuracy and speed of the model by replacing the CSPDarknet backbone of the YOLOv5(s) model with the EfficientViT network. Additionally, the algorithm incorporates the Convolutional Block Attention Module(CBAM) attention mechanism to enhance feature layer information extraction and boost the accuracy of the detection algorithm. To mitigate the adverse effects of low-quality labels on gradient generation and enhance the competitiveness of high-quality anchor frames, a superior gradient gain allocation strategy is employed. Furthermore, the strategy introduces the Wise-IoU (WIoU), a dynamic non-monotonic focusing mechanism for bounding box loss, to further enhance the accuracy and speed of the object detection algorithm. The algorithm's effectiveness is validated through experiments conducted on the 3L-TT100K traffic sign dataset, showcasing a mean average precision (mAP) of 94.1% in traffic sign detection. This mAP surpasses the performance of the YOLOv5(s) algorithm by 4.76% and outperforms the baseline algorithm. Additionally, the algorithm achieves a detection speed of 62.50 frames per second, which is much better than the baseline algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the detection speed of road traffic signs, an improved model based on lightweight YOLOv5 was proposed. Firstly, Ghost convolution and depthwise convolution were used to build a new Bottleneck, which could reduce the amount of computation and parameters. Then the BiFPN structure was introduced, which could enhance the feature fusion ability. CIoU loss function was replaced by SIoU loss function, which focused on the angle information of ground true box and prediction one, so that it would improve the detection accuracy. Secondly, the TT100K dataset was optimized, and 24 categories of traffic sign pictures and labels with more than 200 were screened out. Finally, the experiment achieved 84% accuracy, 81.2% recall and 85.4% mAP@ 0.5. Com pared with the original YOLOv5 model, the number of parameters was reduced by 29.0%, the amount of computation was reduced by 29.4%, but the mAP@ 0.5 was only reduced by 0.1 percentages, and the detection frame rate was improved by 34 frames/ s. Using the improved model for detection, the detection speed could be significantly improved, could basically achieve the goal of compression model on the basis of maintaining the detection accuracy. [ABSTRACT FROM AUTHOR]

Traffic sign detection is one of essential foundations in intelligent transportation and intelligent driving systems. For autonomous driving, traffic signs captured by cameras are usually small, and detecting low resolution imagines of traffic signs at long distances is still a big challenge. To improve the accuracy of small traffic sign detection, this paper proposes an improved lightweight model based on you only look once version 5 small (YOLOv5s). Firstly, a dense connection is employed to reduce the number of parameters in the main network, facilitating multiple reuses of large-scale feature maps to strengthen the ability for extracting information from small targets. Secondly, a new C3 module is constructed by combining the receptive-field attention convolution operation (RFCAConv) mechanism for feature fusion in the neck network to make the network more focused on details of small targets in feature maps. Finally, we replace the original corrected intersection over union (CIOU) loss function with inner-shape intersection over union (Inner-SIOU) loss function, which improves both the training speed and accuracy of the model. Testing results on public traffic signs datasets of CCTSDB2021 and TT100K indicate that the proposed mode reduces the parameter count by 30%, increases mAP0.5 by 4-5%, and boosts FPS by 16% comparing with the original YOLOv5s model.

As autonomous driving technology rapidly advances, the accurate detection and classification of traffic signs have become pivotal in ensuring driver safety and supporting the evolution of autonomous vehicles. Nonetheless, individual models possess inherent limitations. YOLOv8 is renowned for its swift detection capabilities and proficiency in identifying distant objects. However, due to the constraints imposed by its grid cell architecture, it exhibits suboptimal performance in detecting small, close-range targets. Conversely, Mask R-CNN demonstrates high precision in the detection of objects at close range, yet there remains a need for improvement in terms of distant object detection and the speed of detection. To overcome these obstacles, we introduce a novel model which integrates the strengths of Mask Region-based Convolutional Neural Network (Mask R-CNN) and the YOLOv8 model using a stacking ensemble technique. Our model was evaluated on the CCTSDB dataset and the MTSD dataset, demonstrating superior performance across various conditions. The experimental results on the MTSD dataset show a 3.63% improvement in mean Average Precision (mAP) and a 2.35% increase in Frames Per Second (FPS) compared to the Mask R-CNN, achieving a 3.20% increase in mAP over the YOLOv8. Moreover, the proposed model exhibited notable precision in challenging scenarios such as ultra-long-distance detections, shadow occlusions, motion blur, and complex environments with diverse sign categories. These findings not only showcase the model’s robustness but also serve as a cornerstone in propelling the evolution of intelligent transportation systems and autonomous driving technology.

Existing traffic sign detection algorithms suffer from high computational complexity and large parameter sizes, limiting their deployability. The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing the detection of small traffic signs. The integration of StarBlock from StarNet into the C2f module forms the C2f-Star configuration, which simplifies the architecture. In addition, the Slimneck design paradigm is introduced into the neck network to further reduce computational demands while maintaining model accuracy. Subsequently, a Squeeze and Excitations Shared Detection Head (SESDH) is developed to integrate a squeezing and excitation attention mechanism. This design helps diminish the intricacy of the network architecture, increase attention on areas where traffic signs are presence and improve the ability of the model to indicate objects in complex surroundings. Experimental results on the CCTSDB traffic sign dataset reveal that the updated algorithm decreased the number of parameters by 35.33%, reduced the computation by 35.80% and shrunk the model size by 32.94% of the baseline YOLOv8n model while improving the mAP@50 by 1%, and the F1 score by 1.32%. Compared to other algorithms, here, a good balance between accuracy and lightweight design is achieved.

Purposes Aiming at the problems of large computation and high reference quantity in existing traffic sign detection algorithms, in this paper we proposed a lightweight traffic sign detection algorithm based on improved YOLOv7. Methods The algorithm is divided into four parts: input, backbone network of feature extraction, neck network of feature fusion, and head network of target prediction. Large kernel convolution was introduced into the backbone network, which increases the effective receptive field and improves the ability of feature extraction. The detection of neck fusion coordinate attention, random pooling, and other methods can not only build channel attention and capture accurate position, but also improve the generalization ability of the network. In addition, a comprehensive depth-separable convolution module was proposed to extract image features by reducing the number of parameters and the radical sign. Results Experimental results show that the detection accuracy of the proposed algorithm on the CCTSDB2021 data set reaches 93.13%, and mAP also reaches 87.59%, which is a great improvement with respect to other methods of the same type. The network achieves a high accuracy rate under the condition of low parameter number and calculation amount, which can not only accurately capture the location information of traffic signs, but also achieve a high accuracy rate. At the same time, it can accurately predict the traffic signs.