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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2025-06-01
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| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/zh/article/doi/10.11959/j.issn.2096-6652.202518/ |
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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 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 objects 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 solution for foreign object detection on coal conveyor belts and effectively ensuring the safe operation of coal transportation in power plants. |
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| ISSN: | 2096-6652 |