Abstract
Power line corridor inspection plays a vital role in power system safe opera-tion, traditional human inspection’s low efficiency makes the novel inspectionmethod requiring high precision and high efficiency. Combined with thecurrent deep learning target detection algorithm based on high accuracy andstrong real-time performance, this paper proposes a YOLOV4-Tiny baseddrone real-time power line inspection method. The 5G and edge computingtechnology are combined properly forming a complete edge computing archi-tecture. The UAV is treated as an edge device with a YOLOV4-Tiny deep-learning-based object detection model and AI chip on board. Extensive exper-iments on real data demonstrate the 5G and Edge computing architecturecould satisfy the demands of real-time power inspection, and the intelligenceof the whole inspection improved significantly.
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