Transmission Line Faults Detection Algorithm Based on YOLOX

doi:10.3969/j.issn.1006-2475.2024.05.002

Abstract

Abstract: Abstract：Power system is an important foundation of national life， intelligent detection of transmission line faults has great social and economic value. Aiming at the problem of lack of public datasets in transmission line faults detection scenarios， poor performance when there are multiple scale targets simultaneously， and difficulty in detecting high IoU bounding boxes， a transmission line faults detection method based on improved YOLOX was proposed. First， a transmission line faults detection dataset was set up through acquisition and simulation; then an adaptive multi-scale feature fusion method was proposed to fully use multi-scale features; finally a new loss was proposed to improve the optimization ability of the network for high IoU bounding boxes and solve sample imbalance problem， which effectively improved the detection accuracy. The experimental results show that in the dataset collected in this paper， the proposed algorithm can still achieve 67.48% mAP50:95 while ensuring real-time performance， outperforming the classical algorithms such as EfficientDet and YOLOV5.

Key words: Key words: faults detection, YOLOX, adaptive multi-scale fusion, polynomial IoU loss

CLC Number:

TP391.4

WU Hengfeng, HOU Xingsong, WANG Huake. Transmission Line Faults Detection Algorithm Based on YOLOX[J]. Computer and Modernization, 2024, 0(05): 5-10.

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