Automatic Overheating Defect Diagnosis Based on Rotated Detector for Insulator in Infrared Image

Measuring the temperature of insulator in infrared image and diagnosing whether there is abnormal temperature rise are important for electric power inspection. Traditional object detection uses horizontal detection boxes. When they are applied to insulator temperature measurement, their rough positioning results in measurement deviation. Although some detectors with rotated box have been used to tackle this problem, their rotation angle prediction is still not precise enough, resulting in the positioning deviation of insulator core rod, which also leads to temperature measurement deviation. In this article, we propose a complete solution to address these problems. First, we design an adaptive balanced feature pyramid network (Adaptive BFPN) to fuse localization and semantic information more efficiently, which can improve the detection integrity of large-scale insulators. Second, we design a novel
$\theta $
-intersection over union (
$\theta $
-IoU) loss to enhance the accuracy (Acc) of the detector in predicting rotation angle and insulator positioning. Third, we propose a method to locate and automatically correct the position of insulator core rod, measure the temperature, and diagnose defects. It enables faster temperature measurement and more automatic defect diagnosis. Finally, comparative experiments show that our method has a higher accuracy in diagnosing overheating defects than other advanced methods, reaching 97.07%. Our method provides a more intelligent and automated solution for inspecting insulator overheating defects.