GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation

Xie, Guobo; Xu, Zhijun; Lin, Zhiyi; Liao, Xingming; Zhou, Teng

doi:10.1007/s11760-024-03252-8

SIViP

2024

DOI: 10.1007/s11760-024-03252-8

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GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation

Guobo Xie,

Zhijun Xu,

Zhiyi Lin

et al.

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2024

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Cited by 3 publications

References 38 publications

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APNet-YOLOv8s: A real-time automatic aquatic plants recognition algorithm for complex environments

Wang,

Dong,

Yang

et al. 2024

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APNet-YOLOv8s: A real-time automatic aquatic plants recognition algorithm for complex environments

Wang,

Dong,

Yang

et al. 2024

Ecological Indicators

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Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

Xia,

Hu,

Wang

et al. 2024

Electronics

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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.

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YOLOv8s-DDA: An Improved Small Traffic Sign Detection Algorithm Based on YOLOv8s

Niu,

Chen,

et al. 2024

Electronics

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In the realm of traffic sign detection, challenges arise due to the small size of objects, complex scenes, varying scales of signs, and dispersed objects. To address these problems, this paper proposes a small object detection algorithm, YOLOv8s-DDA, for traffic signs based on an improved YOLOv8s. Specifically, the C2f-DWR-DRB module is introduced, which utilizes an efficient two-step method to capture multi-scale contextual information and employs a dilated re-parameterization block to enhance feature extraction quality while maintaining computational efficiency. The neck network is improved by incorporating ideas from ASF-YOLO, enabling the fusion of multi-scale object features and significantly boosting small object detection capabilities. Finally, the original IoU is replaced with Wise-IoU to further improve detection accuracy. On the TT100K dataset, the YOLOv8s-DDA algorithm achieves mAP@0.5 of 87.2%, mAP@0.5:0.95 of 68.3%, precision of 85.2%, and recall of 80.0%, with a 5.4% reduction in parameter count. The effectiveness of this algorithm is also validated on the publicly available Chinese traffic sign detection dataset, CCTSDB2021.

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GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation

Cited by 3 publications

References 38 publications

APNet-YOLOv8s: A real-time automatic aquatic plants recognition algorithm for complex environments

APNet-YOLOv8s: A real-time automatic aquatic plants recognition algorithm for complex environments

Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

YOLOv8s-DDA: An Improved Small Traffic Sign Detection Algorithm Based on YOLOv8s

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