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Underwater target detection is of great significance in underwater ecological assessment and resource development. To better protect the environment and optimize the development of underwater resources, we propose a new underwater target detection model with several innovations based on the YOLOv8 framework. Firstly, the SAConv convolutional operation is introduced to redesign C2f, the core module of YOLOv8, to enhance the network's feature extraction capability for targets of different scales. Secondly, we propose the RFESEConv convolution module instead of the conventional convolution operation in neural networks to cope with the degradation of image channel information in underwater images caused by light refraction and reflection. Finally, we propose an ESPPF module to further enhance the model's multi-scale feature extraction efficiency. Simultaneously, the overall parameters of the model are reduced. Compared to the baseline model, the proposed one demonstrates superior advantages when deployed on underwater devices with limited computational resources. The experimental results show that we have achieved significant detection accuracy on the underwater dataset, with an mAP@50 of 78% and an mAP@50:95 of 43.4%. Both indicators are 2.1% higher compared to the baseline models. Additionally, the proposed model demonstrates superior performance on other datasets, showcasing its strong generalization capability and robustness. This research provides new ideas and methods for underwater target detection and holds important application value. [ABSTRACT FROM AUTHOR]

Various factors such as natural disasters, vandalism, weather, and environmental conditions can affect the physical state of traffic signs. The proposed model aims to improve detection of traffic signs affected by partial occlusion as a result of overgrown vegetation, displaced signs (those knocked down, bent), perforated signs (those damaged with holes), faded signs (color degradation), rusted signs (corroded surface), and de-faced signs (placing graffiti, etc., by vandals). This research aims to improve the detection of bad traffic signs using three approaches. In the first approach, Spiral Pooling Pyramid-Fast (SPPF) and C3TR modules are introduced to the architecture of Yolov5 models. SPPF helps provide a multi-scale representation of the input feature map by pooling at different scales, which is useful in improving the quality of feature maps and detecting bad traffic signs of various sizes and perspectives. The C3TR module uses convolutional layers to enhance local feature extraction and transformers to boost understanding of the global context. Secondly, we use predictions of Yolov5 as base models to implement a mean ensemble to improve performance. Thirdly, test time augmentation (TTA) is applied at test time by using scaling and flipping to improve accuracy. Some signs are generated using stable diffusion techniques to augment certain classes. We test the proposed models on the CCTSDB2021, TT100K, GTSDB, and GTSRD datasets to ensure generalization and use k-fold cross-validation to further evaluate the performance of the models. The proposed models outperform other state-of-the-art models in comparison. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are indispensable in promoting the development of remote sensing technology. Nevertheless, the tasks of object recognition in remote sensing images based on UAV platforms face major difficulties and challenges due to the complex and variable background environments and the high-density distribution of objects. This paper proposes an object detection algorithm for UAV remote sensing images—YOLO-Remote, which aims to improve detection accuracy by enhancing YOLOv8. This algorithm innovatively integrates the SaElayer module to enhance the focus on remote sensing targets and improve network efficiency. Additionally, it introduces the Efficient-SPPF structure, which effectively expands the network’s receptive field and promotes deep learning capabilities. To address sample imbalance and improve bounding box localization and classification performance, the study also designs the Focaler-MDPIOU strategy. With these comprehensive optimizations, YOLO-Remote achieves significant progress in network architecture. Experiments were conducted on the NWPU VHR10 and RSOD datasets, and the experimental results show that compared to the base model YOLOv8n, the improved model’s average precision increased by 2.7% and 3.2% respectively, demonstrating its superiority in the field of object detection for UAV remote sensing images.The code is available at https://github.com/QuincyQAQ/Yolo-Remotehttps://github.com/QuincyQAQ/Yolo-Remote.

Accurate face recognition technology is of great significance for face anti-counterfeiting. Due to illumination, posture, angle, and other reasons, the existing face liveness detection technology is difficult to adapt the environmental changes, resulting in low detection accuracy. To address this issue, this paper presents a novel high-performance face anti-spoofing detection method named RGCS_ConvNeXt. The data-enhanced face images are fed into the ConvNext network, which group convolution is added to extract the correlation between different features, and the coordinate attention mechanism is used to enhance the facial feature extraction capability both spatially and directionally. Then SPPF is used to extract the features at different scales to enhance the representation of the feature map. Finally, the facial key point detection technique is utilized to calculate the eye EAR value to achieve accurate face anti-counterfeiting recognition. The proposed algorithm shows an average classification error rate of 0.3%, 1.7%, 1.9%±1.5% and 2.8%±3.4%, respectively, on the four protocols of the OULU-NPU public dataset. On the Siw dataset, the average classification error rate is 0.69%, a reduction of 0.02% compared to the MA-Net network. The half-error rate on the MSU-MFSD dataset is 2.39%, a 0.21% reduction compared to the DPCNN network. The algorithm shows good accuracy on the OULU-NPU, MSU-MFSD and Siw datasets, reaching 99.64%, 98.40% and 99.25% respectively, 0.26% higher than the SE-FeatherNet network’s average accuracy.

Underwater target detection is of great significance in underwater ecological assessment and resource development. To better protect the environment and optimize the development of underwater resources, we propose a new underwater target detection model with several innovations based on the YOLOv8 framework. Firstly, the SAConv convolutional operation is introduced to redesign C2f, the core module of YOLOv8, to enhance the network’s feature extraction capability for targets of different scales. Secondly, we propose the RFESEConv convolution module instead of the conventional convolution operation in neural networks to cope with the degradation of image channel information in underwater images caused by light refraction and reflection. Finally, we propose an ESPPF module to further enhance the model’s multi-scale feature extraction efficiency. Simultaneously, the overall parameters of the model are reduced. Compared to the baseline model, the proposed one demonstrates superior advantages when deployed on underwater devices with limited computational resources. The experimental results show that we have achieved significant detection accuracy on the underwater dataset, with an mAP@50 of 78% and an mAP@50:95 of 43.4%. Both indicators are 2.1% higher compared to the baseline models. Additionally, the proposed model demonstrates superior performance on other datasets, showcasing its strong generalization capability and robustness. This research provides new ideas and methods for underwater target detection and holds important application value.

Various factors such as natural disasters, vandalism, weather, and environmental conditions can affect the physical state of traffic signs. The proposed model aims to improve detection of traffic signs affected by partial occlusion as a result of overgrown vegetation, displaced signs (those knocked down, bent), perforated signs (those damaged with holes), faded signs (color degradation), rusted signs (corroded surface), and de-faced signs (placing graffiti, etc., by vandals). This research aims to improve the detection of bad traffic signs using three approaches. In the first approach, Spiral Pooling Pyramid-Fast (SPPF) and C3TR modules are introduced to the architecture of Yolov5 models. SPPF helps provide a multi-scale representation of the input feature map by pooling at different scales, which is useful in improving the quality of feature maps and detecting bad traffic signs of various sizes and perspectives. The C3TR module uses convolutional layers to enhance local feature extraction and transformers to boost understanding of the global context. Secondly, we use predictions of Yolov5 as base models to implement a mean ensemble to improve performance. Thirdly, test time augmentation (TTA) is applied at test time by using scaling and flipping to improve accuracy. Some signs are generated using stable diffusion techniques to augment certain classes. We test the proposed models on the CCTSDB2021, TT100K, GTSDB, and GTSRD datasets to ensure generalization and use k-fold cross-validation to further evaluate the performance of the models. The proposed models outperform other state-of-the-art models in comparison.

Urban underground pipelines are an important infrastructure in cities, and timely investigation of problems in underground pipelines can help ensure the normal operation of cities. Owing to the growing demand for defect detection in urban underground pipelines, this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector (FCOS), called spatial pyramid pooling-fast (SPPF) feature fusion and dual detection heads based on FCOS (SDH-FCOS) model. This study improved the feature fusion component of the model network based on FCOS, introduced an SPPF network structure behind the last output feature layer of the backbone network, fused the local and global features, added a top-down path to accelerate the circulation of shallowinformation, and enriched the semantic information acquired by shallow features. The ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection heads. The experimental results using an open dataset of underground pipes show that the proposed SDH-FCOS model can recognize underground pipe defects more accurately; the average accuracy was improved by 2.7% compared with the original FCOS model, reducing the leakage rate to a large extent and achieving real-time detection. Also, our model achieved a good trade-off between accuracy and speed compared with other mainstream methods. This proved the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The CA-YOLO (Coordinate Attention-YOLO) model has been optimized for object detection in complex remote sensing images, addressing key issues faced by algorithms that detect multiple objects. These issues include weak multi-scale feature learning capabilities and the challenging trade-off between detection accuracy and model parameter complexity. The CA-YOLO model, built on the framework of YOLOv5, incorporates a lightweight coordinate attention module in the shallow layer to improve detailed feature extraction and suppress redundant information interference. Additionally, a spatial pyramid pooling-fast with a tandem construction module is implemented in the deeper layer. The model employs a stochastic pooling strategy to fuse multi-scale key feature information from low-level to high-level layers, reducing the number of model parameters while improving inference speed. We optimized the anchor box mechanism and modified loss function to improve the ability of the model to detect objects of different sizes and scales. Results show that the CA-YOLO model outperforms the original YOLO in terms of multi-object detection accuracy, with an average mAP@0.5 accuracy improvement of 4.8% and mAP@0.5:0.95 accuracy improvement of 3.8%. Additionally, the CA-YOLO model demonstrates exceptional inference speed, averaging 125 fps, which reinforces its superiority in detection accuracy, generalization ability, and overall efficiency. Notably, these improvements were achieved while maintaining the same number of parameters and complexity as other models, making the CA-YOLO model an exceptional choice for various applications.

With the rapid development of remote sensing technology, remote sensing target detection faces many problems; for example, there is still no good solution for small targets with complex backgrounds and simple features. In response to the above, we have added dynamic snake convolution (DSC) to YOLOv7. In addition, SPPFCSPC is used instead of the original spatial pyramid pooling structure; the original loss function was replaced with the EIoU loss function. This study was evaluated on UAV image data (VisDrone2019), which were compared with mainstream algorithms, and the experiments showed that this algorithm has a good average accuracy. Compared to the original algorithm, the mAP0.5 of the present algorithm is improved by 4.3%. Experiments proved that this algorithm outperforms other algorithms.

In view of the problems of multi‐scale, densely distributed and difficult small target detection in real‐time road condition detection, a new improved yolov5 real‐time target detection method was proposed. The activation function in the original spatial pyramid (SPPF) was replaced by ReLu in the backbone network to enhance the expression ability of the model. The bidirectional feature pyramid (BiFPN) is used to replace the original feature pyramid network (FPN) + pixel aggregation network (PAN) in the neck area. The bidirectional feature fusion improves the utilization of multi‐scale semantic features and strengthens the extraction of image deep features. The convolutional attention mechanism (CBAM) is introduced to further improve the feature extraction ability of the algorithm and make the algorithm pay more attention to useful information. The results of self‐made traffic condition data set show that the average accuracy of the improved yolov5 model reaches 81.3%, which is 10.9% higher than that of the original yolov5 model, showing better detection accuracy and real‐time performance. Compared with some mainstream target detection algorithms, this algorithm has certain advantages. [ABSTRACT FROM AUTHOR]

The data is not always shared among sub‐networks due to concerns about information privacy or difficulties in synchronous information exchange. It is difficult or even impossible to obtain the probabilistic power flow (PPF) by the centralised analysis. In this paper, an equivalent model considering static power–frequency characteristics (SPFCs) and renewable uncertainties is proposed. In this model, SPFCs of equivalent loads and generators are derived to retain SPFCs of original external loads and generators, respectively. In addition, probabilistic characteristics and correlations of equivalent loads are formulated based on Cholesky decomposition to preserve original external renewable uncertainties. The proposed model is further applied to PPF and then PPF can be solved via Monte Carlo simulation method, which makes that the PPF results can be obtained when the detailed data of the original external network cannot be shared. Owing to efficiently preserving the external SPFCs and renewable uncertainties in the proposed equivalent model, the accuracy of PPF results can be guaranteed. Simulation results of IEEE 14‐bus and IEEE‐118 bus systems demonstrate the effectiveness and superiority of the proposed equivalent model and its applications to PPF, compared with existing well known equivalent models and their applications to PPF. [ABSTRACT FROM AUTHOR]