Abstract:

In transmission line inspection tasks， utilizing deep learning technology to track the movement of construction machinery effectively is crucial for smart grid construction. To address the issue of significant performance degradation in multi-object tracking caused by occlusion among targets and false or missed detections， a multi-object tracking algorithm combining improved YOLOv5s and optimized ByteTrack was proposed. In the object detection section： firstly， lightweight Ghost convolution and SimAM were used to construct the SGC3 （SimAM and Ghost convolution with C3） module， thereby improving feature utilization and reducing redundant computations in the algorithm. Secondly， in deeper layers of the backbone network， a convolution-guided triplet attention module R-Triplet （RFAConv with Triplet attention） was proposed， thereby using a multi-branch structure to enhance cross-dimensional information interaction of the algorithm and suppress irrelevant background information to improve object association capability. Finally， in the feature fusion stage， a Multi-branch Receptive Block （MRB） was added， thereby utilizing dilated convolution to expand the receptive field of the object and enhancing reuse of multi-scale global feature information of the object. In the object tracking section： based on ByteTrack algorithm， according to motion characteristics of construction machinery， an NSA （Noise Scale Adaptively） Kalman filter algorithm with adaptive noise scale computation was proposed to decrease the influence of low-quality detection boxes on filtering performance. At the same time， Gaussian Smoothing Interpolation （GSI） algorithm was introduced into the data association process to further optimize multi-object tracking performance. Experimental results indicate that compared to the baseline algorithm YOLOv5s， the proposed CRM-YOLOv5s algorithm achieves mean Average Precision （mAP） of 97.4%， which is improved by 3.8 percentage points with the of parameters and floating-point operations reduced by 0.28×10⁶ and 1.8 GFLOPs， respectively， demonstrating stronger generalization capability in various application scenarios. Additionally， compared to the original YOLOv5s+ByteTrack tracking algorithm， after combining with improved ByteTrack， the proposed CRM-YOLOv5s algorithm has the Multiple Object Tracking Accuracy （MOTA） increased by 4.5 percentage points， the number of Identity switches （IDs） decreased by 15， and higher inference speed， demonstrating that the algorithm is suitable for multi-object tracking task of construction machinery in transmission line scenarios.

Key words: transmission line scenario, object detection, multi-object tracking, YOLOv5s, ByteTrack

摘要：

在输电线路巡检任务中，采用深度学习技术实现施工机械运动的有效跟踪对智能电网建设至关重要。针对目标间遮挡干扰以及误检漏检造成的多目标跟踪性能显著下降的问题，提出一种改进YOLOv5s与优化ByteTrack相结合的多目标跟踪算法。在目标检测部分：首先，采用轻量级的Ghost卷积和SimAM构建SGC3 （SimAM and Ghost convolution with C3）模块，以提高特征利用率，并减少算法冗余计算；其次，在主干网络的深层，提出卷积引导的三重注意力模块R-Triplet（RFAConv with Triplet attention），从而利用多分支结构增强算法跨维度信息交互，并抑制不相关背景信息来提高目标的关联能力；最后，在特征融合部分添加多分支感受野模块（MRB），以利用空洞卷积扩大目标感受野，并增强多尺度目标全局特征信息的复用。在目标跟踪部分：在ByteTrack算法的基础上，根据施工机械的运动特点，提出一种自适应计算噪声尺度的NSA（Noise Scale Adaptively）卡尔曼滤波算法，以降低低质量检测框对滤波算法性能的影响；同时，在数据关联部分引入高斯平滑插值算法（GSI），从而进一步完善多目标跟踪的效果。实验结果表明，所提CRM-YOLOv5s算法的平均精度均值（mAP）达到了97.4%，与基线算法YOLOv5s相比提升了3.8个百分点，参数量和浮点运算量分别减少了0.28×10⁶和1.8 GFLOPs，可见该算法在多种应用场景下的泛化能力更强。此外，相较于原YOLOv5s+ByteTrack跟踪算法，所提CRM-YOLOv5s算法与改进后的ByteTrack算法相结合后的多目标跟踪准确度（MOTA）提升了4.5个百分点，目标身份切换次数（IDs）减少了15，且获得了较高的推理速度，可见该算法适用于输电线路场景下施工机械的多目标跟踪任务。

关键词: 输电线路场景, 目标检测, 多目标跟踪, YOLOv5s, ByteTrack