Abstract: To address the challenge of local texture features in mineral images containing complex defects such as micro-fractures,mineral impurities,layered cracks,and irregularly fractured regions,this paper proposes an improved YOLOv11 object detection model named YOLOv11-CSW.This model integrates lightweight backbone network feature extraction module CIR,lightweight neck structure Slim-neck,and WIoU(Weighted Intersection over Union ) loss function.Firstly,an IRMB(Inverted Resi-dual Mobile Block) module is introduced into the backbone network and enhanced with a CGA(Cascaded Group Attention) mo-dule to reduce the number of model parameters and computational cost.Secondly,a lightweight Slim-neck module is incorporated to redesign the neck part,improving multi-scale feature fusion capabilities while maintaining parameter efficiency.Finally,the WIoU loss function is employed to accurately compute the loss and accelerate the bounding box regression process.Compared with the original YOLOv11 algorithm,experiments on an ore dataset show that the improved method reduces the number of parameters by 6.2% and computational load by 8%,while increasing mAP₅₀ by 2.9%.The proposed model not only improves detection accuracy but also reduces model parameters,achieving a balance between lightweight design and performance,making it suitable for real-time ore recognition and detection systems.

Key words: Ore image, Backbone network module, Feature fusion network, Loss function, Lightweight