智能科学与技术学报2025，Vol.7Issue(2)：268-276,9.DOI:10.11959/j.issn.2096-6652.202518
注意力机制增强的输煤传送带异物检测
Foreign object detection on coal conveyor belt enhanced by attention mechanism
张杨 1程智宇 2陈允降 1张建南 1袁文胜 1张慧2
作者信息
- 1. 国家电投集团协滨海发电有限公司,江苏 盐城 224553
- 2. 北京交通大学计算机科学与技术学院,北京 100044
- 折叠
摘要
Abstract
There are many complex factors in the special environment of coal transportation in power plants,such as un-even light,dust interference,and the different shapes,sizes,and materials of foreign objects on the coal conveyor belt.In this complex environment,many current target detection algorithms are not sensitive enough to the characteristics of for-eign objects,and it is difficult to effectively distinguish foreign objects with different characteristics.In order to solve this problem,the network structure of the original YOLOv8 algorithm was optimized and a YOLOv8-CPCA detection method was proposed.The feature extraction ability of the model was significantly improved by introducing the channel prior con-volutional attention mechanism(CPCA),and high-precision detection of foreign objects in the harsh environment of coal transportation in power plants was achieved.A unique combination of convolution and pooling operations was used by the CPCA attention mechanism to perform global average pooling and maximum pooling on the input feature map,multi-dimensional feature information was deeply mined,and then attention weights for each channel and spatial position were accurately generated through nonlinear transformation,guiding the model to focus on the key feature areas of foreign ob-jects and enhance feature extraction capabilities.Experimental results show that the improved model not only ensures the real-time detection,but also increases the average detection accuracy mAP@0.5 to 92.9%,providing a more accurate solu-tion for foreign object detection on coal conveyor belts and effectively ensuring the safe operation of coal transportation in power plants.关键词
YOLOv8/注意力机制/CPCA/输煤传送带/异物检测Key words
YOLOv8/attention mechanism/CPCA/coal conveyor belt/foreign object detection分类
信息技术与安全科学引用本文复制引用
张杨,程智宇,陈允降,张建南,袁文胜,张慧..注意力机制增强的输煤传送带异物检测[J].智能科学与技术学报,2025,7(2):268-276,9.
