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Most object detection models cannot achieve satisfactory performance under nighttime and other insufficient illumination conditions, which may be due to the collection of data sets and typical labeling conventions. Public data sets collected for object detection are usually photographed with sufficient ambient lighting. However, their labeling conventions typically focus on clear objects and ignore blurry and occluded objects. Consequently, the detection performance levels of traditional vehicle detection techniques are limited in nighttime environments without sufficient illumination. When objects occupy a small number of pixels and the existence of crucial features is infrequent, traditional convolutional neural networks (CNNs) may suffer from serious information loss due to the fixed number of convolutional operations. This study presents solutions for data collection and the labeling convention of nighttime data to handle various types of situations, including in-vehicle detection. Moreover, the study proposes a specifically optimized system based on the Faster region-based CNN model. The system has a processing speed of 16 frames per second for 500 × 375-pixel images, and it achieved a mean average precision (mAP) of 0.8497 in our validation segment involving urban nighttime and extremely inadequate lighting conditions. The experimental results demonstrated that our proposed methods can achieve high detection performance in various nighttime environments, such as urban nighttime conditions with insufficient illumination, and extremely dark conditions with nearly no lighting. The proposed system outperforms original methods that have an mAP value of approximately 0.2.

An autonomous vehicle must accurately detect its surrounding environment to operate reliably. Adverse weather conditions (ADWC) are snow, rain, sand, and haze, badly affect the quality of vehicle detection (VD) in an autonomous environment. Most existing techniques focused on VD under various weather effects such as signal control, travel pattern, traffic volume variations and collision risk. Only a limited number of works of literature were focused on VD under ADWC at different automation scales. In this paper, a novel deep learning (DL) model, Optimized You Look Only Once Version 5 (OYOLOV5), is proposed for autonomous VD (AVD) in ADWC. The proposed model consists of three phases: data collection, data preprocessing, feature extraction and classification. Initially, the data is collected from the DAWN and COCO dataset to perform VD, which is openly available. The augmentation of the data is carried out on the collected input data by including hue, saturation, blur, brightness, and noise, which helps to get a clear view of vehicles. After data augmentation, feature extraction and classification of the preprocessed images are done using the OYOLOV5 framework, which uses Resnet-50 as the backbone network and Feature Pyramid Network (FPN) for detecting the vehicles at multi-scales. Experiments are conducted, and the outcomes demonstrated the proposed OYOLOV5 model achieves better performance with the state-of-art methods in terms of precision (PRC), recall (RC), f-measure (FMS), accuracy (ACU), average IoU (AI), processing speed (PS), and training time (TTI). Also, the system attains good mean average precision (mAP) than the conventional methods. [ABSTRACT FROM AUTHOR]

Automated object detection has received the most attention over the years. Use cases ranging from autonomous driving applications to military surveillance systems, require robust detection of objects in different illumination conditions. State-of-the-art object detectors tend to fare well in object detection during daytime conditions. However, their performance is severely hampered in night light conditions due to poor illumination. To address this challenge, the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime conditions. Firstly, the preprocessing strategies involve using the Zero- DCE++ approach to enhance lowlight images. It is followed by optimizing the existing YOLOv5 architecture by integrating the Convolutional Block AttentionModule (CBAM) in the backbone network to boost model learning capability and Depthwise Convolutional module (DWConv) in the neck network for efficient compression of network parameters. The Night Object Detection (NOD) and Exclusively Dark (ExDARK) dataset has been used for this work. The proposed framework detects classes like humans, bicycles, and cars. Experiments demonstrate that the proposed architecture achieved a higher Mean Average Precision (mAP) along with a reduction in model size and total parameters, respectively. The proposedmodel is lighter by 11.24% in terms ofmodel size and 12.38% in terms of parameters when compared to baseline YOLOv5. [ABSTRACT FROM AUTHOR]

Featured Application: This paper proposes a deep-learning-based vehicle detection technique that can achieve effective detection performance under extreme illumination conditions. The technique can be used in on-road driver assistance tools and autonomous vehicles. Most object detection models cannot achieve satisfactory performance under nighttime and other insufficient illumination conditions, which may be due to the collection of data sets and typical labeling conventions. Public data sets collected for object detection are usually photographed with sufficient ambient lighting. However, their labeling conventions typically focus on clear objects and ignore blurry and occluded objects. Consequently, the detection performance levels of traditional vehicle detection techniques are limited in nighttime environments without sufficient illumination. When objects occupy a small number of pixels and the existence of crucial features is infrequent, traditional convolutional neural networks (CNNs) may suffer from serious information loss due to the fixed number of convolutional operations. This study presents solutions for data collection and the labeling convention of nighttime data to handle various types of situations, including in-vehicle detection. Moreover, the study proposes a specifically optimized system based on the Faster region-based CNN model. The system has a processing speed of 16 frames per second for 500 × 375-pixel images, and it achieved a mean average precision (mAP) of 0.8497 in our validation segment involving urban nighttime and extremely inadequate lighting conditions. The experimental results demonstrated that our proposed methods can achieve high detection performance in various nighttime environments, such as urban nighttime conditions with insufficient illumination, and extremely dark conditions with nearly no lighting. The proposed system outperforms original methods that have an mAP value of approximately 0.2. [ABSTRACT FROM AUTHOR]