A Confidence Calibration Based Ensemble Method for Oriented Electrical Equipment Detection in Thermal Images

Ying Lin, Zhuangzhuang Li, Bo Song, Ning Ge, Yiwei Sun and Xiaojin Gong ()
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Ying Lin: State Grid Shandong Electric Power Research Institute, Jinan 250002, China
Zhuangzhuang Li: State Grid Shandong Electric Power Research Institute, Jinan 250002, China
Bo Song: State Grid Shandong Electric Power Research Institute, Jinan 250002, China
Ning Ge: State Grid Shandong Electric Power Company, Jinan 250013, China
Yiwei Sun: State Grid Shandong Electric Power Research Institute, Jinan 250002, China
Xiaojin Gong: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2025, vol. 18, issue 12, 1-12

Abstract: Detecting oriented electrical equipment plays a fundamental role in enabling intelligent defect diagnosis in power systems. However, existing oriented object detection methods each have their own limitations, making it challenging to achieve robust and accurate detection under varying conditions. This work proposes a model ensemble approach that leverages the complementary strengths of two representative detectors—Oriented R-CNN and S 2 A-Net—to enhance detection performance. Recognizing that discrepancies in confidence score distributions may negatively impact ensemble results, this work first designs a calibration method to align the confidence levels of predictions from each model. Following calibration, a soft non-maximum suppression (Soft-NMS) strategy is employed to fuse the outputs, effectively refining the final detections by jointly considering spatial overlap and the calibrated confidence scores. The proposed method is evaluated on an infrared image dataset for electric power equipment detection. Experimental results demonstrate that our approach not only improves the performance of each individual model by 1.95 mean Average Precision (mAP) but also outperforms other state-of-the-art methods.

Keywords: electrical equipment detection; oriented object detection; model ensemble; confidence calibration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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