黑龙江科技大学学报2024，Vol.34Issue(1)：112-117,6.DOI:10.3969/j.issn.2095-7262.2024.01.017
基于全局自注意力机制的煤矸石目标检测网络
Coal gangue object detection network based on global self-attention mechanism
摘要
Abstract
This paper is focused on the solution to the problem of low recognition accuracy caused by high similarity of coal and coal gangue in the process of coal gangue detection and proposes a coal gangue object detection network based on global self-attention mechansim.The study involves introducing the global context block into YOLOv5s networ to capture long-distanced feature,enlarge receptive field,in-troduce omni-dimensional dynamic convolution by adding self-attention mechanism;and improving the network performance further as well as controling the computing load.The experimental results show that the improved YOLOv5s network is superior to the original network and the control group networks.Com-pared with the network YOLOv5s,YOLOv5m,YOLOv5l,YOLOv7 和YOLOv7x,the object detection ac-curacies of the coal gangue are improved by 4.1%,3.2%,2.1%,2.6%and 2%respectively.This model with high speed can meet the requirement of real-time detection.关键词
煤矸石/深度学习/YOLOv5/注意力机制Key words
coal gangue/deep learning/YOLOv5/attention mechanism分类
矿业与冶金引用本文复制引用
汝洪芳,李作淘,王国新,王书侠..基于全局自注意力机制的煤矸石目标检测网络[J].黑龙江科技大学学报,2024,34(1):112-117,6.基金项目
2023年黑龙江省省属高等学校基本科研业务费科研项目(2023-KYYWF-0545) （2023-KYYWF-0545）
黑龙江省省重点研发计划指导类项目(GZ20220122) （GZ20220122）
