Abstract: UAVs have significant advantages over traditional measurement and remote-sensing technologies in monitoring open-pit mining areas. At present, existing UAV-based detection methods for spontaneous combustion in open-pit coal seams mainly suffer from the lack of corresponding detection models capable of identifying high-temperature points, low recognition accuracy for small-size and multi-scale high-temperature points, and confusion between exhaust-pipe high temperatures of excavators and spontaneous combustion high-temperature points on coal seams. To address these problems, a YOLO-WRC-based detection method for spontaneous combustion in open-pit coal seams using UAV imagery was proposed. Wavelet Transform Convolution (WTConv) was integrated into the backbone network to focus on richer feature information; a Reparameterized Generalized Feature Pyramid Network (RepGFPN) was used to reconstruct the neck network,enhance the ability of feature extraction and fusion and the recognition accuracy of easily confused high temperature points; a Concentrated Layerwise Localization Attention Head (CLLAHead) was introduced to coordinate feature and semantic information across different levels, focusing on the identification of micro high-temperature points; and the PIoUv2 loss function was adopted to improve the model's regression performance for multi-scale abnormal high-temperature points. The experimental results showed that ① the accuracy, recall, and mAP@0.5 of YOLO-WRC reached 88.2%, 90.1%, and 95.4%, respectively, which were 1.3%, 2.2%, and 3.2% higher than those of the original YOLOv8n model. ② The recall and mAP@0.5 of YOLO-WRC were superior to mainstream models such as SSD, Faster-RCNN, YOLOv5, and YOLOv10n, demonstrating high robustness and adaptability in identifying abnormal high-temperature points. ③ YOLO-WRC yielded higher confidence for detection targets and identified targets missed by YOLOv8n, exhibiting stronger recognition capability for easily confused and small-sized targets.