Foreign object detection on coal conveyor belt enhanced by attention mechanism

There are many complex factors in the special environment of coal transportation in power plants, such as uneven 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 algorithm...

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Bibliographic Details
Published in智能科学与技术学报 Vol. 7; pp. 268 - 276
Main Authors ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui
Format Journal Article
LanguageChinese
Published POSTS&TELECOM PRESS Co., LTD 01.06.2025
Subjects
attention mechanism
coal conveyor belt
CPCA
foreign object detection
YOLOv8
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ISSN2096-6652

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Summary:There are many complex factors in the special environment of coal transportation in power plants, such as uneven 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 foreign 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 convolutional 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
ISSN:2096-6652