Vehicle target detection algorithm based on improved YOLOv3

Weijun Liu Sr.; Weimin Zhou Sr.; Jingxiang Cui Sr.

doi:10.1117/12.2637514

13 May 2022 Vehicle target detection algorithm based on improved YOLOv3

Weijun Liu Sr., Weimin Zhou Sr., Jingxiang Cui Sr.

Proceedings Volume 12248, Second International Conference on Sensors and Information Technology (ICSI 2022); 122481H (2022) https://doi.org/10.1117/12.2637514
Event: 2nd International Conference on Sensors and Information Technology (ICSI 2022), 2022, Nanjing, China

Abstract

The detection of road vehicle targets is an important research direction in the field of target detection. As there is some problem in vehicle target detection while using YOLOv3, such as high miss rate and low detection accuracy, an improved multi-scale feature fusion target detection algorithm IFFC-YOLOv3 is proposed. Firstly, optimizing the multi-scale feature fusion based on the YOLOv3 algorithm to improve the detection of small targets while ensuring the detection capability of the network for medium and large targets. Secondly, using K-Means++ target frame clustering on road target detection turns out to obtain new candidate frames to improve its accuracy. Finally, the CIoU optimized loss function is introduced to further improve the accuracy of vehicle target detection. In this paper, the effect of the improved IFFC-YOLOv3 algorithm is verified by means of a personal datasets. The experimental results show that the IFFC-YOLOv3 algorithm achieves good detection results in terms of reducing the rate of missed detection and improving the detection accuracy.

Citation Download Citation

Weijun Liu Sr., Weimin Zhou Sr., and Jingxiang Cui Sr. "Vehicle target detection algorithm based on improved YOLOv3", Proc. SPIE 12248, Second International Conference on Sensors and Information Technology (ICSI 2022), 122481H (13 May 2022); https://doi.org/10.1117/12.2637514

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KEYWORDS

Target detection

Detection and tracking algorithms

Roads

Prototyping

Algorithm development

Statistical modeling

Feature extraction

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