Research on target detection algorithm based on vehicle detection

Yanguo Huang; Zehao Rao; Luo Li

doi:10.1117/12.3014382

9 January 2024 Research on target detection algorithm based on vehicle detection

Yanguo Huang, Zehao Rao, Luo Li

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129690K (2024) https://doi.org/10.1117/12.3014382
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Aiming at the current problem of unsatisfactory vehicle detection in complex scenes, an improved vehicle target detection network model is proposed. First, Res2Net residual network is fused in SCP, and the CSP_R structure is proposed, so that the model can extract deeper feature information and strengthen the ability to characterize small-scale targets; the attention mechanism is introduced, and the C3_CBAM module is designed to strengthen the attention to the detection targets while avoiding the increase of the model's computational volume; the loss function of the MPDIoU regression optimization is introduced, and the loss function is optimized by combining the prediction frame with the real frame length, width and area loss, and quantitative indicators to improve the convergence speed and robustness of the model. Finally, the model is validated on the SODA10M dataset, and the experimental results show that the model detection speed reaches 32 frames per second. The average detection accuracy reaches 83.7%, which is an improvement of 7.8 percentage points compared with YOLOV5s.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanguo Huang, Zehao Rao, and Luo Li "Research on target detection algorithm based on vehicle detection", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129690K (9 January 2024); https://doi.org/10.1117/12.3014382

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