Feature Extraction Network Architecture.

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.

针对港口码头视频图像中因尺度变化、背景干扰及目标遮挡导致的定位精度下降问题，本研究提出一种融合YOLOv5s与DeepSORT的增强型模型，集成目标载荷识别与轨迹跟踪功能，以提升对码头场景的适配性。研究结果表明，在YOLOv5s中引入多尺度卷积可提升多尺度目标检测的鲁棒性，使平均精度均值（mean Average Precision, mAP）提升0.4%。此外，集成高效金字塔分割注意力网络（efficient pyramid segmentation attention, EPSA）可增强多尺度特征融合表征的准确性，引入EPSA后模型的mAP@0.5:0.95提升1.2%。最后，本研究采用分布式排序损失方法优化原分类损失函数，以缓解码头图像序列中载荷目标类别失衡及背景变化带来的负面影响，该优化使多目标跟踪精度（multi-target tracking accuracy, MOTA）提升3.1%。在自主构建的数据集上的实验结果表明，该模型平均准确率达90.9%，检测准确率达92.2%，可为港口码头场景下的目标识别与跟踪任务提供重要参考。