Research on detection methods of high beam in night driving vehicles

Yuchun Chu; Hang Gong; Xue-fang Wang; Peishun Liu

doi:10.1117/12.2623207

16 February 2022 Research on detection methods of high beam in night driving vehicles

Yuchun Chu, Hang Gong, Xue-fang Wang, Peishun Liu

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120832L (2022) https://doi.org/10.1117/12.2623207
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

In order to solve the problem that it is difficult to control the abuse of high beam at night, a high beam detection algorithm YOLO-CAU based on YOLOv4 is designed and implemented. Then, aiming at the problem that it is difficult deployed on devices with limited computing power with a complex neural network, we apply the model compression algorithm of knowledge distillation to the image object detection task, and conducts distillation training on the YOLOCAU model with the high beam data set, transferring the knowledge learned by YOLO-CAU to a simple network (YOLO-CAU-S) with less computation and faster detection speed. Compared with the existing knowledge distillation algorithm, the algorithm designed in this paper not only restricts the output of the student network to be close to the output of the teacher, but also proposes a multi-scale pixel attention module to construct the attention feature maps of the teacher model and student model, which further transfers the strong attention of the teacher network to the student. The experimental results show that the knowledge distillation algorithm designed in this paper can greatly improve the detection efficiency of the model on the premise of ensuring the detection accuracy, and the compressed model parameters are only 6% of the teacher network.

Citation Download Citation

Yuchun Chu, Hang Gong, Xue-fang Wang, and Peishun Liu "Research on detection methods of high beam in night driving vehicles", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120832L (16 February 2022); https://doi.org/10.1117/12.2623207

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KEYWORDS

Detection and tracking algorithms

Data modeling

Feature extraction

Target detection

Image enhancement

Convolution

Evolutionary algorithms

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