An Improved Object Detection Method for Automatic Electrical Equipment Defect Detection

Rui Guo, Shaosheng Fan, Youmin Li, Zhiyuan Liu, Kai Liu

Abstract


Employing computer vision and machine learning on equipment defect detection has become an important trend of electric power inspection. This paper presents a new electrical equipment default detection method based on improved YOLOv3. By combining abundant geometric measures, Complete IOU(CIOU) make the bounding box regression during NMS more accurate. Focal loss function, which focus on differentiate between easy and hard examples, is employed to deal with the class imbalance problem. Experiment results show that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.


DOI
10.12783/dteees/peees2020/35491

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