Abstract: With the rapid development of smart mines,the real-time and precise identification of large rocks in the blasting operation for ore loading has become a key requirement for ensuring transportation safety and efficiency.To address the challenges posed by highly irregular shapes,significant overlap among particles,low image resolution,and sparse features in the images of the blasted heap ore,this paper proposes a lightweight image segmentation algorithm for blasted heap ore that achieves a balance between accuracy and efficiency through multi-dimensional model optimization.Firstly,the topological characteristics of the DynamicHGNetv2(Dynamic High Performance GPU Network version2) hierarchical graph network are utilized to reconstruct the backbone network,compressing redundant features through a dynamic routing mechanism,which reduces the model size by 42.4%.Secondly,the HSFPN(High-level Screening-feature Fusion Pyramid) is designed as the neck network,employing a multi-scale feature screening mechanism guided by channel attention,which reduces the computational load by 27.4% while enhancing the ability for cross-scale feature fusion.Subsequently,a lightweight segmentation head is constructed,optimizing computational efficiency further through depthwise separable convolutions and feature distillation techniques.Finally,the EMASlideLoss(Exponential Moving Average SlideLoss) loss function is introduced,dynamically adjusting the weights of difficult samples based on an exponential moving average strategy,significantly improving the model's edge segmentation accuracy for low-quality ore targets.Experimental results indicate that,compared to the YOLO11n-seg benchmark model,the proposed method reduces the number of parameters and computational costs by 42.4% and 27.4%,respectively,while mAP50 and mAP50:95 improve by 0.1% and 2.1%,respectively.This not only meets the needs for high-precision real-time segmentation in mining scenarios but also can be directly deployed on edge computing devices,providing reliable technical support for early warning systems for large ore in smart shoveling systems.

Key words: Open-pit mine, Bast heap, Image segmentation, YOLO11, Lightweight network