Mine hazardous obstacle segmentation for automated bulldozer with segment anything model

Ensuring the safety of autonomous bulldozers in open-pit mines requires accurate detection of hazardous obstacles such as sumps, stones, and hollows. However, detecting these hazards is challenging due to low contrast with the background, blurry boundaries, and variations in texture, shape, size, and distribution. To address these challenges, this paper proposes Segment Anything Model based mine hazardous obstacle detection model (Mine-SAM). Mine-SAM enhances segmentation performance for hazardous objects through the Weighted Mixture Adapters structure (WMix adapter), Dual Attention Mechanism (DAT), and Wavelet convolutions-based Receptive Field Blocks (WT_RFB). On a self-constructed dataset spanning multiple scenes, Mine-SAM achieved a mean Intersection over Union (mIoU) of 0.9158 and maintained an inference speed exceeding 12 FPS (Frames Per Second) for video. Analysis of detection results across adverse operational scenarios and construction stages demonstrates the model's stability and reliable detection performance, highlighting its practical value in ensuring the safety of autonomous bulldozers during operation.