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Port terminal mobile recognition based on combined YOLOv5s-DeepSort

Chengzhi Wang, Donghong Chen, Zhen Liu, Yuanhao Li, Yifei Wang and Sanglan Zhao

PLOS ONE, 2025, vol. 20, issue 7, 1-23

Abstract: To solve the problem of reduced positioning accuracy caused by changes in scale, background and occlusion in port and dock video images, this research proposes an enhanced model combining YOLOv5s-DeepSORT, integrating target load recognition and trajectory tracking to improve adaptability to dock environments. The findings indicate that incorporating multi-scale convolution into YOLOv5s improved the robustness of multi-scale object detection, resulting in a 0.4% increase in mean Average Precision (mAP). Furthermore, the integration of an efficient pyramid segmentation attention (EPSA) network enhanced the accuracy of multi-scale feature fusion representation. The model’s mAP@0.5:0.95 increased by 1.2% following the introduction of EPSA. Finally, the original classification loss function was enhanced using a distributed sorting loss approach to mitigate the imbalance among loaded objects and the influence of background variations in the dock image sequence. This optimization led to a 3.1% improvement in multi-target tracking accuracy (MOTA). Experimental results on self-constructed datasets demonstrated an average accuracy of 90.9% and a detection accuracy of 92.2%, offering a valuable reference for target recognition and tracking in port and dock environments.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0326376

DOI: 10.1371/journal.pone.0326376

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