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

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 S2A-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.