DMFR-YOLO: an infrared small hotspot detection algorithm based on double multi-scale feature fusion

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Abstract Hotspot automatic detection is an effective strategy to realize intelligent maintenance of photovoltaic (PV) modules. However, it is challenging to detect small hotspots accurately using deep learning-based object detection methods due to the weak information and small area of the target. This study presents a double multi-scale feature reconstruction (DMFR)-YOLO to detect small hotspots in PV modules. In the proposed DMFR-YOLO, the backbone of YOLOv8n is improved by eliminating redundant deep layers and introducing a shallow detection head to enhance its perception and recognition ability for small objects. Moreover, a weighted multi-layer feature reconstruction (MLFR) module is introduced to fuse the features of different depths and a multi-receptive field feature reconstruction (MRFR) module is designed to fuse the information of different receptive fields. Finally, ablation and comparison experiments are conducted to evaluate the performance of the proposed method. The model with both MLFR and MRFR can achieve a recall rate of 88.6%, with a mean average precision (mAP@0.5) of 93.3%. Experimental results demonstrate that the proposed DMFR-YOLO is able to achieve small hotspot detection in IR images with high accuracy.