- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
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| Citation: | HU Xin, SHAO Liangbin, ZHOU Yun-qiang, XIAO Jian, CHENG Hongliang. Vehicle Detection and Tracking Algorithm for Distant Small Targets Based on Improved YOLOv5s-CSC and Deep-Sort[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240528
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To address the problems of missed detection of distant small vehicle targets, as well as ID switching and jumping during tracking in traffic surveillance scenarios, a small-target detection and tracking algorithm combining improved You Only Look Once version 5 small (YOLOv5s) and improved deep simple online and realtime tracking (Deep-Sort) was proposed. For detection, based on the YOLOv5s baseline network, the convolutional block attention module (CBAM) mechanism was introduced to enhance the feature capture of small targets. The Swin Transformer Block was fused with the cross stage partial (CSP) structure in the backbone network to construct the CSPSTB module and strengthen global information attention. The content-aware reassembly of features (CARAFE) upsampling operator was adopted to reduce feature loss, forming the YOLOv5s-CSC detection network. For tracking, a deep appearance feature extraction network containing a residual squeeze-and-excitation (Res_SE) structure was designed, which was more adaptable to vehicle features during data association. Experimental results on the UA-DETRAC dataset indicate that, compared with the original YOLOv5s model, the improved detection algorithm increases the mean average precision (
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