Real-Time and Efficient Multi-Scale Traffic Sign Detection Method for Driverless Cars

Wang, Xuan; Guo, Jian; Song, Yongchao; Xu, Jindong; Yan, Weiqing; Fu, Xin

doi:10.3390/s22186930

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…M-YOLO detects and classifies traffic signs, addressing detection in intricate environments. Wang et al [ 105 ] propose real-time multi-scale TSD for driverless cars. The multi-scale approach detects traffic signs of various sizes, enhancing performance in diverse scenarios.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

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Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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“…Detecting and identifying occluded and camouflaged objects in low-illumination … (Gaytri Bakshi) 189 Traffic analysis and management systems constitute a significant area of study, wherein researchers endeavor to effectively regulate road traffic and devise strategies to mitigate congestion and prevent vehicular accidents. Various attempts have been undertaken to establish systems aimed at standardizing traffic regulations and apprehending individuals who violate them [6], [7]. Autonomous vehicles represent a prominent forthcoming solution.…”

Section: Issn: 2252-8814 mentioning

confidence: 99%

Detecting and identifying occluded and camouflaged objects in low-illumination environments

Bakshi,

Aggarwal

2024

IJAAS

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One of the prevailing areas of contemporary research involves the differentiation and identification of diverse objects within a given scene through automated systems. The field of study under consideration presents a multitude of obstacles, including but not limited to issues such as diminished lighting conditions, occlusion, and camouflage. The captured image exhibits variations in illumination, resulting in uneven brightness, reduced contrast, and the presence of noise. The fundamental basis of computer vision algorithms lies in the process of extracting features from datasets and subsequently discerning these features through neural networks. The task of extracting distinct feature key points from images captured under low lighting conditions is exceedingly challenging. To address this issue, the present study seeks to employ deep learning models to implement image enhancement techniques specifically designed for low-light conditions. The primary emphasis lies in obtaining key feature points that are differentiable, thereby enabling the utilization of this annotated data for specific tasks such as object detection. The task of identifying occluded and camouflaged objects has been successfully accomplished, yielding an impressive accuracy rate of 93% in total. The mean average precision has been achieved as 85% which is reasonably high compared to many earlier works.

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“…Precision/% Recall/% mAP/% YOLOv5s [24] 76.13 76.75 77.64 YOLOv5s+BiFPN [25] 87.82 84.39 88.26 MS-YOLOv5s [26] 95.04 94.86 96.31…”

Section: Modelmentioning

confidence: 99%

Micro-Leakage Image Recognition Method for Internal Detection in Small, Buried Gas Pipelines

Zhao

2023

Sensors

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In order to resolve the problem that the sample of image for internal detection of DN100 buried gas pipeline microleakage is single and difficult to identify, a recognition method of microleakage image of the pipeline internal detection robot is proposed. First, nongenerative data augmentation is used to expand the microleakage images of gas pipelines. Secondly, a generative data augmentation network, Deep Convolutional Wasserstein Generative Adversarial Networks (DCWGANs), is designed to generate microleakage images with different features for detection in the pipeline of gas pipelines to achieve sample diversity of microleakage images of gas pipelines. Then, a bi-directional feature pyramid network (BiFPN) is introduced into You Only Look Once (YOLOv5) to retain more deep feature information by adding cross-scale connecting lines in the feature fusion structure; finally, a small target detection layer is constructed in YOLOv5 so that more shallow feature information can be retained to achieve small-scale leak point recognition. The experimental results show that the precision of this method for microleak identification is 95.04%, the recall rate is 94.86%, the mAP value is 96.31%, and the minimum size of identifiable leaks is 1 mm.

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Real-Time and Efficient Multi-Scale Traffic Sign Detection Method for Driverless Cars

Cited by 12 publications

References 4 publications

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Detecting and identifying occluded and camouflaged objects in low-illumination environments

Micro-Leakage Image Recognition Method for Internal Detection in Small, Buried Gas Pipelines

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