YOLO-EDH: An Enhanced Ore Detection Algorithm

Mineral identification technology is a key technology in the construction of intelligent mines. In ore classification and detection, mining scenarios present challenges, such as diverse ore types, significant scale variations, and complex surface textures. Traditional detection models often suffer from insufficient multi-scale feature representation and weak dynamic adaptability, leading to the missed detection of small targets and misclassification of similar minerals. To address these issues, this paper proposes an efficient multi-scale ore classification and detection model, YOLO-EDH. To begin, standard convolution is replaced with deformable convolution, which efficiently captures irregular defect patterns, significantly boosting the model’s robustness and generalization ability. The C3k2 module is then combined with a modified dynamic convolution module, which avoids unnecessary computational overhead while enhancing the flexibility and feature representation. Additionally, a content-guided attention fusion (HGAF) module is introduced before the detection phase, ensuring that the model assigns the correct importance to various feature maps, thereby highlighting the most relevant object details. Experimental results indicate that YOLO-EDH surpasses YOLOv11, improving the precision, recall, and mAP50 by 0.9%, 1.7%, and 1.6%, respectively. In conclusion, YOLO-EDH offers an efficient solution for ore detection in practical applications, with considerable potential for industries like intelligent mine resource sorting and safety production monitoring, showing notable commercial value.