LPT-Net: A Line-Pad Transformer Network for efficiency coal gangue segmentation with linear multi-head self-attention mechanism

Abstract

Accurately separating coal and gangue is a crucial step in coal production. However, existing methods often face efficiency challenges. The high computational complexity hinders real-time gangue sorting. Real-time segmentation is essential for improved sorting results, making efficiency a crucial evaluation metric. Additionally, ensuring real-time segmentation accuracy is crucial. To address these issues, we propose Line-Pad Transformer Network (LPT-Net), a efficient segmentation network for gangue sorting. LPT-Net incorporates L-MSA, a module designed for linear attention calculation. It utilizes linear feature sampling for efficient computation, enabling the rapid extraction of semantic features from coal gangue. Additionally, LPT-Net introduces MSA1 and MSA2, serving as semantic information providers to enable more effective extraction of gangue semantic features. Experimental results on GSTD demonstrate LPT’s effectiveness, achieving 74.71 IoU and 83.85 Acc at 95.7 FPS, outperforming other methods by 1.5–12 times in terms of inference speed. These results highlight LPT’s suitability for efficient gangue sorting tasks.