Class-imbalanced traffic sign recognition based on improved YOLOv7

Zhuo Wei; Jiaoxiong Xia

doi:10.1117/12.2680960

8 June 2023 Class-imbalanced traffic sign recognition based on improved YOLOv7

Zhuo Wei, Jiaoxiong Xia

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073H (2023) https://doi.org/10.1117/12.2680960
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

As the transportation industry continues to grow and traffic conditions become increasingly complex, ensuring driving safety has become a pressing issue of concern. Traffic sign recognition can offer real-time evaluation of road conditions and driving safety alerts for vehicles and ultimately reduce traffic safety accidents. This study proposes a class-imbalanced traffic sign recognition method based on improved YOLOv7. Firstly, the Coordinate Attention is incorporated into the YOLOv7 backbone to enhance the representation and localization ability of the target to be tested. Then, the larger feature layer is introduced to improve the recognition ability. Finally, the class imbalance problem in the dataset is addressed by using Cost-sensitive softmax cross-entropy loss. The improved YOLOv7 network has been shown through experiments to enhance the detection capability of traffic signs effectively, reaching 90.54% mAP and 86.78% F1-Score.

Citation Download Citation

Zhuo Wei and Jiaoxiong Xia "Class-imbalanced traffic sign recognition based on improved YOLOv7", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073H (8 June 2023); https://doi.org/10.1117/12.2680960

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KEYWORDS

Object detection

Small targets

Convolution

Data modeling

Feature extraction

Safety

Target detection

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