工矿自动化2026，Vol.52Issue(8)：26-36,11.DOI:10.13272/j.issn.1671-251x.2026040074
基于改进RT-DETR的井下胶带断裂预警研究
Early warning of underground conveyor belt breakage based on improved RT-DETR
摘要
Abstract
Early warning of underground conveyor belt breakage faces challenges including the poor interference resistance of single-modality signals,difficulties in capturing early microdamage features,and high redundancy in multi-source heterogeneous data fusion.Existing multimodal fusion methods remain inadequate in jointly utilizing video,audio,operating condition,and environmental information during belt breakage evolution.To address these challenges,a Top-k cross-modal feature enhancement module was introduced into RT-DETR to construct a Top-k cross-modal feature-enhanced RT-DETR model.Temporal alignment at the data level and dimensional unification at the feature level were performed on the four types of information,establishing cross-modal associations anchored to visual features and addressing the asynchrony and heterogeneity of multi-source data.A Top-k sparse attention mechanism was introduced to dynamically select auxiliary features closely associated with breakage evolution during cross-modal interactions,effectively suppressing contamination of fused features by noise in complex underground environments.The enhanced multiscale visual features were fed into the RT-DETR detection framework to enable progressive state recognition from microdamage to precursors of breakage.A complete early-warning decision pipeline was then constructed,incorporating state classification,quantitative risk scoring,four-level warning threshold assessment,and temporal sliding-window voting for noise suppression.Experimental results showed that the model achieved an early-warning accuracy of 90.7％and an F1 score of 0.893 on the test set,significantly outperforming comparison models such as the standard Transformer and YOLOv8.Compared with the original DETR framework,accuracy improved by 4.4％,inference time decreased by 40％,and the number of parameters decreased by approximately 20％.Accuracy remained above 88％under high dust concentrations and sharp temperature and humidity fluctuations.The model significantly improves inference efficiency while maintaining high detection accuracy and retains good early-warning stability under extreme operating conditions,providing effective technical support for intelligent operation and maintenance of belt conveyors under complex operating conditions.关键词
胶带断裂预警/多源异构数据/多模态特征融合/跨模态特征增强/RT-DETR/Top-k稀疏注意力机制Key words
conveyor belt breakage early warning/multi-source heterogeneous data/multimodal feature fusion/cross-modal feature enhancement/RT-DETR/Top-k sparse attention mechanism分类
矿业与冶金引用本文复制引用
张冬阳,李柯,贺耀宜,安渊..基于改进RT-DETR的井下胶带断裂预警研究[J].工矿自动化,2026,52(8):26-36,11.基金项目
国家自然科学基金面上项目(52274159). （52274159）
