Abstract: To address the problems of blurred boundaries of hot-spot targets,complex temperature gradient variations,and large differences in target scales in infrared image object detection for electrical equipment in mining-area substations,existing detection algorithms generally have deficiencies such as insufficient recognition accuracy and high missed detection rates. By comparing the performance of several mainstream object detection algorithms on an infrared image dataset of electrical equipment in mining-area substations,it is found that the Faster R-CNN algorithm incorporating a feature pyramid structure performs relatively well,but its recognition performance of small hot-spot targets and temperature anomaly regions still has room for improvement. Therefore,an improved Faster R-CNN algorithm is proposed. First,a spatial pyramid pooling structure is introduced into the network to extract multi-scale features. Second,the feature fusion method is optimized to enhance the network′s perception of targets at different scales. In the region proposal generation stage,the quality of region proposals is improved by expanding anchor scales and aspect ratios. Finally,the classification and bounding box regression loss functions are improved to enhance detection accuracy. Experimental results show that,compared with the original Faster R-CNN algorithm,the improved algorithm achieves improvements of 2. 7 percentage points in AP(0. 5 ∶0. 95),4. 3 percentage points in AP(0. 5),and 1. 8 percentage points in the detection accuracy for small-sized hot-spot targets on the infrared image dataset of electrical equipment in mining-area substations,reflecting that the improved algorithm has certain applicability to infrared detection of mining-area electrical equipment.