Vehicle detection based on improved YOLOv4

Xiaoqiang Yang; Bowen Chen

doi:10.1117/12.2636527

6 May 2022 Vehicle detection based on improved YOLOv4

Xiaoqiang Yang, Bowen Chen

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121761B (2022) https://doi.org/10.1117/12.2636527
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

With the development of deep learning, the performance of vehicle detection algorithms based on deep learning is constantly improved, which plays an important role in the construction of intelligent transportation. Single-stage target detection model is widely used in vehicle real-time detection because of its advantages of detection speed. In view of the low detection rate of small objects in images, this article proposes a vehicle object detection method based on the improved YOLOv4 algorithm, using k-means clustering algorithm to re-create a anchors suitable for the UA-Detrac dataset and improve the PANet. Compared with other target detection methods, the improved algorithm can effectively detect small targets and improve the Precision, Recall and mAP of vehicle targets.

Citation Download Citation

Xiaoqiang Yang and Bowen Chen "Vehicle detection based on improved YOLOv4", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121761B (6 May 2022); https://doi.org/10.1117/12.2636527

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