CR-YOLOv8: Multiscale Object Detection in Traffic Sign Images

Zhang, Lu Jia; Fang, Jian Jun; Liu, Yan Xia; Feng Le, Hai; Rao, Zhi Qiang; Zhao, Jia Xiang

doi:10.1109/access.2023.3347352

Cited by 18 publications

(2 citation statements)

References 47 publications

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“…Compared with the method in this study, YOLOv5(l) has a higher detection accuracy, and according to the comprehensive analysis of Tables 4 and 6, the algorithm in this paper has the advantages of high detection accuracy and speed. In order to prove that the detection performance of this paper's algorithm is better than the existing state-of-the-art traffic sign detection algorithms, the algorithm in this paper is compared with ETSR-YOLO [30], TRD-YOLO [31], and CR-YOLOv8 [32], and the accuracy of traffic sign detection is better than the three traffic sign target detection algorithms mentioned above.…”

Section: Comparative Analysis Of Algorithm In Real-timementioning

confidence: 99%

Efficient Vision Transformer YOLOv5 for Accurate and Fast Traffic Sign Detection

Zeng,

Wu,

et al. 2024

Electronics

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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.

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Section: Comparative Analysis Of Algorithm In Real-timementioning

confidence: 99%

Efficient Vision Transformer YOLOv5 for Accurate and Fast Traffic Sign Detection

Zeng,

Wu,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Faster-RCNN 63.4 28.3 -83 SSD [16] 75.1 26.1 80.9 24 STC-YOLO [26] 88.9 6.70 -87 Improved YOLOv5 [35] 81.9 7.51 16.8 30 SEDG-YOLOv5 [36] 91.0 2.77 6.2 178 YOLOv7-tiny [38] 93.4 23.29 -67 CR-YOLOv8 [43] 86.9 14.60 -103 CTM-YOLOv8n 94.3 0.89 9.7 161…”

Section: Model Map@050 (%) Params (M) Gflops Fpsmentioning

confidence: 99%

CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization

Chen,

Dai,

et al. 2024

WEVJ

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Traffic-sign detection and recognition (TSDR) is crucial to avoiding harm to pedestrians, especially children, from intelligent connected vehicles and has become a research hotspot. However, due to motion blurring, partial occlusion, and smaller sign sizes, pedestrian TSDR faces increasingly significant challenges. To overcome these difficulties, a CTM-YOLOv8n model is proposed based on the YOLOv8n model. With the aim of extracting spatial features more efficiently and making the network faster, the C2f Faster module is constructed to replace the C2f module in the head, which applies filters to only a few input channels while leaving the remaining ones untouched. To enhance small-sign detection, a tiny-object-detection (TOD) layer is designed and added to the first C2f layer in the backbone. Meanwhile, the seventh Conv layer, eighth C2f layer, and connected detection head are deleted to reduce the quantity of model parameters. Eventually, the original CIoU is replaced by the MPDIoU, which is better for training deep models. During experiments, the dataset is augmented, which contains the choice of categories ‘w55’ and ‘w57’ in the TT100K dataset and a collection of two types of traffic signs around the schools in Tianjin. Empirical results demonstrate the efficacy of our model, showing enhancements of 5.2% in precision, 10.8% in recall, 7.0% in F1 score, and 4.8% in mAP@0.50. However, the number of parameters is reduced to 0.89M, which is only 30% of the YOLOv8n model. Furthermore, the proposed CTM-YOLOv8n model shows superior performance when tested against other advanced TSDR models.

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

Xie,

Xu,

Lin

et al. 2024

SIViP

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CR-YOLOv8: Multiscale Object Detection in Traffic Sign Images

Cited by 18 publications

References 47 publications

Efficient Vision Transformer YOLOv5 for Accurate and Fast Traffic Sign Detection

Efficient Vision Transformer YOLOv5 for Accurate and Fast Traffic Sign Detection

CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization

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

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