GLD-YOLO: An Ore Instance Segmentation Algorithm for Underground Crushing Scenes

Accurate ore identification is a prerequisite for automated crushing by robotic arms. However, the complex underground mining environment, along with the inherent characteristics of ore such as complex shapes, subtle textures, and blurred boundaries, severely restricts ore identification performance. To address these issues, this paper proposes an improved instance segmentation model, GLD-YOLO, based on the YOLO11-seg network. First, considering the feature aliasing problem in underground ore segmentation, a collaborative enhancement mechanism of C3k2_GBC and C2PSA_LRSA is introduced. By adopting novel gated convolution and local self-attention, the edge and detail features of the target are effectively enhanced, strengthening the saliency of the ore’s effective features. Second, addressing the limitation that deep networks easily lose subtle edge information, a PAFPN_DDFE neck network is constructed. Through the novel DDFE module, shallow features are effectively utilized, and the advantages of spatial and frequency domain information are integrated to achieve noise filtering and edge information enhancement. Then, to further refine the boundaries, the gradient update path is optimized using the Focaler-Shape-IoU loss function, guiding the network to focus on difficult samples and improving the regression accuracy of the ore bounding boxes. Finally, comparative experiments and analyses were conducted between the proposed improved method and the benchmark algorithm by building an ore crushing simulation experimental platform. The results show that the proposed method improves the mAP50 and mAP50-95 metrics by 4.4% and 5.1%, respectively, compared to the benchmark model, and the model detection speed reaches 90 FPS. On the MineralImage5k and Crack-seg datasets, the mAP50 is improved by 1.9% and 2.1%, respectively, verifying the feasibility and generalization of the recognition model.