煤矿安全2025，Vol.56Issue(3)：224-232,9.DOI:10.13347/j.cnki.mkaq.20241414
结合改进CNN与自注意力机制的煤矿轮式机器人目标检测技术
Target detection technology of coal mine wheeled robot combining improved CNN and self attention mechanism
摘要
Abstract
In complex coal mine environment and poor lighting conditions,the existing target detection technology is difficult to meet the needs of intelligent inspection.To solve this problem,an object detection method based on improved convolutional neural network and self-attention mechanism is proposed.Firstly,a feature extraction network based on pyramid structure and attention mechanism is constructed.On this basis,the bidirectional feature pyramid network module is designed to further strengthen the fea-ture extraction function.Finally,the YOLO Head module is used for prediction processing.The test results show that after 2 398 iter-ations,the loss of the model is stabilized at about 0.01,and the ideal loss effect is achieved.The accuracy of the model reached 0.95 at 1 598 iterations and entered steady state at about 1 845 iterations,which was one of the fastest models to reach steady state.The overall detection time was 3.2 ms.The model can improve the accuracy and efficiency of target detection in complex environment.关键词
煤矿轮式机器人/智能巡检/目标检测算法/金字塔结构/注意力机制/深度学习Key words
coal mine wheeled robot/intelligent inspection/object detection algorithm/pyramid structure/attention mechanism/deep learning分类
矿业与冶金引用本文复制引用
唐俊飞,邢海龙,李溯,张涛涛,刘恒,姚诗雨..结合改进CNN与自注意力机制的煤矿轮式机器人目标检测技术[J].煤矿安全,2025,56(3):224-232,9.基金项目
国家重点研发计划资助项目(2022YFB4703600) （2022YFB4703600）
2023年辽宁省人工智能创新发展计划重大专项资助项目(2023JH26/10100006) （2023JH26/10100006）
中煤科工集团科技创新基金资助项目(2023-QN003) （2023-QN003）
