Research on YOLO-v3 road target detection based on the combination of K-means++ algorithm and cross-entropy loss function

Zongze Du; Jiangbei Su; Jiafeng Ding; Zhengchun Liu

doi:10.1117/12.2638594

23 May 2022 Research on YOLO-v3 road target detection based on the combination of K-means++ algorithm and cross-entropy loss function

Zongze Du, Jiangbei Su, Jiafeng Ding, Zhengchun Liu

Author Affiliations +

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 1225435 (2022) https://doi.org/10.1117/12.2638594
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

The accurate detection of targets on the road is of great significance (for the big data of road system). However, the traditional detection suffers from missed detection and false detection. In this work, an improved algorithm was proposed based on Yolo-v3 algorithm. The replacement of traditional K-means clustering algorithm with K-means++ algorithm endowed the system a more suitable size of detection frame. Meanwhile, the detection sensitivity of small or incomplete targets was obviously improved with the utilization of cross entropy loss function. The detection experiments on vehicles and pedestrians were performed in two actual situations, normal situation and congested situation. The average detection accuracy was increased by 2.46% with the improvement of clustering algorithm, and the accuracy was increased by 2.08% with the improvement of loss function. The average accuracy was increased by 3.56% with the combination of K-means++ algorithm and cross-entropy loss. The experiments results showed that the combined algorithm possessed a significant improvement in average accuracy, recall and detection speed. This research provides new ideas for the establishment of high-precision algorithms for road dynamic target detection.

Citation Download Citation

Zongze Du, Jiangbei Su, Jiafeng Ding, and Zhengchun Liu "Research on YOLO-v3 road target detection based on the combination of K-means++ algorithm and cross-entropy loss function", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 1225435 (23 May 2022); https://doi.org/10.1117/12.2638594

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