Using attention mechanism and improved YOLOX vehicle taillight detection

Shujin Zhao; Chongyang Zhang

doi:10.1117/12.2679419

7 June 2023 Using attention mechanism and improved YOLOX vehicle taillight detection

Shujin Zhao, Chongyang Zhang

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 1270103 (2023) https://doi.org/10.1117/12.2679419
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

Taillight detection is of great significance and value in predicting driving intention of vehicle ahead in assisted driving and unmanned driving systems. Aiming at the problem that the vehicle taillight detection model YOLOX is not able to detect small targets and the channel information does not contain location information, a taillight detection algorithm based on the attention mechanism and improved YOLOX is proposed. Firstly, a micro-scale detection layer is added to further multi-scale feature fusion to extract more taillight feature information. Secondly, the mobile network attention mechanism is inserted into the proposed improved feature fusion network to extract taillight position information. Finally, the sensing field area at the output end of feature extraction layer is enlarged to improve the detection accuracy of small targets such as taillights. The taillight data set established in natural environment was used, and the number of samples was 1,962 images. The training set, verification set and test set were randomly divided in 8:1:1 ratio to verify the improved network. Experimental results show that the improved model has a 91.71% average detection accuracy (mAP) for taillight images, which is 3.48% higher than the standard YOLOX algorithm.

Citation Download Citation

Shujin Zhao and Chongyang Zhang "Using attention mechanism and improved YOLOX vehicle taillight detection", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 1270103 (7 June 2023); https://doi.org/10.1117/12.2679419

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KEYWORDS

Taillights

Feature extraction

Signal detection

Feature fusion

Object detection

Image fusion

Target detection

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