Early Warning Method of Transmission Corridor Hazards Based on Object Detection and Edge Segmentation
Zhao Zhenbing1,2,3, Fu Longmei1, Pan Yitian1, Li Haopeng1,2
1. Department of Electronic and Communication Engineering North China Electric Power University Baoding 071003 China; 2. Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Baoding 071003 China; 3. Engineering Research Center of Intelligent Computing for Complex Energy Systems of Ministry of Education North China Electric Power University Baoding 071003 China
Abstract：As a critical carrier for power transmission, transmission lines are frequently exposed to high safety risks and recurrent accident hazards due to their characteristics of wide distribution, extensive coverage, long distances, and prolonged exposure to outdoor environments. In recent years, with the rapid development of deep learning technologies, image recognition based on deep learning has provided innovative solutions for transmission line inspection. However, challenges such as complex transmission corridor scenarios, significant variations in target scales, and low detection accuracy for mechanical external damage targets under occluded conditions often lead to false positives and missed detections. Moreover, merely identifying mechanical targets is insufficient to meet the comprehensive requirements of hazard warning, as it necessitates integrating spatial location information of transmission lines for holistic risk assessment. To address these issues, this paper proposes a hazard detection method for transmission corridors based on edge segmentation and object detection. By incorporating a small target detection layer and an SBA module into YOLOv8, the method significantly enhances multi-scale feature representation and target localization capabilities through selective aggregation of boundary and semantic information, adaptive attention mechanisms, and bidirectional feature fusion, demonstrating superior performance in small target detection. Additionally, the adoption of re-parameterized lightweight heads and re-parameterizable convolutions reduces the number of parameters while improving parameter utilization, effectively compensating for potential accuracy loss due to lightweight design and providing a lossless optimization solution for resource-constrained devices. Furthermore, the improvement of CIoU using MPDIoU further enhances detection accuracy. This paper also employs a segmentation network for power line extraction and introduces a multi-level early warning discrimination mechanism based on pixel overlap ratio (OR) and spatial distance, aiming to improve the accuracy and reliability of hazard target detection in power field operation scenarios. By fusing the hazard target detection bounding boxes output by the object detection network with the transmission corridor regions extracted by the segmentation network, the system leverages their pixel coordinates for localization and systematically analyzes their topological relationships in pixel space. The overlap ratio (OR) between the hazard target bounding box and the transmission corridor region is defined as the core indicator for warning triggers. When an overlap exists, an OR-based warning mechanism is activated, with spatial distance further introduced as an auxiliary metric to assess the threat level of the hazard target. The ablation experiments demonstrate that our proposed strategies achieve 72.1% mAP50, representing a 3.2 percentage points improvement over the baseline model. Comparative experiments confirm that our model outperforms YOLOv5, YOLOv7, YOLOv8, YOLOv10, and YOLOv11. Visualization results indicate that the enhanced model effectively reduces missed detections and false positives by focusing more precisely on hazard target features. The warning system evaluation proves that our segmentation-based approach for power line edge extraction provides superior foreground-background differentiation. The proposed warning method not only detects potential hazards but also accurately assesses their threat level to transmission corridors in 2D space. Experimental results show that the proposed method can accurately identify various external damage targets in transmission corridors while quantitatively assessing the threat level of potential hazards. The excellent performance of this method in both target identification and threat analysis provides key technical support for intelligent monitoring and maintenance of modern power infrastructure, effectively promoting the innovative development of smart grid technologies.
赵振兵, 付龙美, 潘逸天, 李浩鹏. 基于目标检测与边缘分割的输电走廊隐患预警方法[J]. 电工技术学报, 2026, 41(3): 987-998.
Zhao Zhenbing, Fu Longmei, Pan Yitian, Li Haopeng. Early Warning Method of Transmission Corridor Hazards Based on Object Detection and Edge Segmentation. Transactions of China Electrotechnical Society, 2026, 41(3): 987-998.
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2026, Vol. 41 
