| 摘 要: 随着我国电力行业的飞速发展,传统的人工电力巡检方式已无法满足当前行业的发展需求。文章提出一种基于深度学习的电力巡检目标检测与追踪模型。该模型通过在YOLOv7中引入CBAM(Convolutional Block Attention Module)注意力模块,构建了CBAM-YOLOv7改进检测算法,并将其识别结果作为DeepSORT(Simple Online and Realtime Tracking With A Deep Association Metric)目标追踪算法的输入,实现了对电网故障的有效检测与追踪。实验结果表明,相较于原YOLOv7算法,改进后的CBAM-YOLOv7算法在精确度、召回率、平均精度3个评价指标上均有提升,而DeepSORT算法的平均MOTA值也达到87.817%。这证明了该模型能够在真实复杂场景下准确地定位电网故障。 |
| 关键词: 电力巡检;深度学习;YOLOv7;CBAM;DeepSORT |
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中图分类号: TP391.4
文献标识码: A
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| 基金项目: 河南省大学生创新创业训练项目(202310919011) |
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| Research on Power Inspection Target Detection and Tracking Based on Deep Learning |
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LI Shenyang, YU Heng, DENG Wenshuai, CHEN Xiaohang, YANG Chen
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(School of In f ormation Engineering, PingDingShan University, Pingdingshan 467000, China)
lsy373@163.com; 2540@pdsu.edu.cn; shuaidyb@gmail.com; 1485317034@qq.com; 1790867636@qq.com
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| Abstract: With the rapid development of the power industry in China, traditional manual power inspection methods can no longer meet the current industry demands. This paper proposes a deep learning-based model for object detection and tracking in power inspection. The model introduces the CBAM (Convolutional Block Attention Module) attention mechanism into YOLOv7, creating an improved detection algorithm called CBAM-YOLOv7. The detection results from this model are used as input for the DeepSORT (Simple Online and Realtime Tracking With A Deep Association Metric) tracking algorithm, achieving effective detection and tracking of power grid faults. Experimental results indicate that compared to the original YOLOv7 algorithm, the improved CBAM-YOLOv7 algorithm shows enhancements in precision, recall, and mean average precision metrics, while the DeepSORT algorithm achieves an average MOTA value of 87.817% . It is proved that the model can accurately locate power grid faults in real complex scenarios. |
| Keywords: power inspection; deep learning; YOLOv7; CBAM; DeepSORT |
