A Hidden Danger Object Detection Method for Transmission Line Corridor Based on YOLO-2MCS
Zheng Hanbo1, Hu Sijia1, Liang Yanshen1, Huang Junjie2, Wang Tao2
1. Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning 530004 China; 2. State Grid Hubei Electric Power Research Institute Wuhan 430077 China
Abstract：Transmission lines are vulnerable to external breakage when crossing high-speed railways, highways and important transmission channels (referred to as “Three-Span”). The occurrence of such accidents is highly random, and may causes damage to transmission equipment, power grid tripping, power outages, and even accidental electric shock and other safety accidents, which seriously affect the safe, reliable and stable operation of transmission line. The existing super-high/UHV transmission line inspection methods include manual inspection, robot inspection, helicopter inspection and UAV inspection, etc., and gradually form the maintenance and operation mode with UAV inspection as the main and manual inspection as the supplement. However, UAV inspection also has limitations such as difficult operation, limited flight distance, and many uncertainties in the field, which is not suitable for large-scale promotion. Aiming at the limitations of the existing transmission line inspection methods and the lack of the external force damage object dataset, this paper collected and constructed a hidden danger object dataset of transmission line corridor, which contains three typical hidden danger object categories and 8 654 targets in total. The hybrid data augmentation strategy is used to effectively enrich the dataset to improve the generalization and robustness of the model in complex scenarios and avoid the model overfitting problem during training caused by a single scenario. The convolutional block attention module (CBAM) is introduced in the EfficientRep backbone network to reduce the feature loss in the extraction process of the original backbone network and improve the model's ability to identify and locate occluded objects. The bidirectional feature pyramid network using the softplus activation function enhance the feature learning ability of the model and the convergence speed during training. The SIoU loss function that defines the angle cost function is introduced in the detector, which can make the loss function in the training process converge as soon as possible and complete the parameter adjustment of backpropagation, so that the model can obtain better and faster object positioning performance. The results of ablation experiments showed that the strategies proposed in this paper can significantly improve the detection accuracy and detection speed of the model without unduly affecting the complexity of the model in the hidden danger object dataset of transmission line corridor constructed in this paper. Compared with the original YOLOv6 model, the average accuracy of the new model is improved by 4.4% under the strict threshold of 0.5:0.95, the average detection speed is as high as about 300 frames per second, and the memory size is only 40.7 MB. The results of comparative experiments showed that the detection accuracy and detection speed of the proposed method are significantly better than the Faster R-CNN, YOLOX, YOLOv5 and YOLOv7 under the same hyperparameters and the same dataset. The visualization results showed that when there is a hidden danger object that are occluded, the proposed model can also better identify and locate it. Based on the above experimental results, the YOLO-2MCS model proposed in this paper can not only accurately identify the categories of external force damage in the transmission line corridor monitoring scene, but also quickly and accurately locate the hidden danger object, all while meeting the needs of devices installed on the mobile edge. The hidden danger object dataset of transmission line corridor constructed in this paper provides effective support for subsequent model training and iteration, and effectively develops the intelligent development of the prevention of external force damage in transmission line corridor.
郑含博, 胡思佳, 梁炎燊, 黄俊杰, 汪涛. 基于YOLO-2MCS的输电线路走廊隐患目标检测方法[J]. 电工技术学报, 2024, 39(13): 4164-4175.
Zheng Hanbo, Hu Sijia, Liang Yanshen, Huang Junjie, Wang Tao. A Hidden Danger Object Detection Method for Transmission Line Corridor Based on YOLO-2MCS. Transactions of China Electrotechnical Society, 2024, 39(13): 4164-4175.
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