Foreign object detection on coal conveyor belt enhanced by attention mechanism

ZHANG Yang; CHENG Zhiyu; CHEN Yunjiang; ZHANG Jiannan; YUAN Wensheng; ZHANG Hui

doi:10.11959/j.issn.2096-6652.202518

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Foreign object detection on coal conveyor belt enhanced by attention mechanism

Papers and Reports | 更新时间：2025-07-21

- Foreign object detection on coal conveyor belt enhanced by attention mechanism
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 2, Pages: 268-276(2025)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202518
  CLC： TP391
- Received：19 February 2025，
  
  Revised：2025-05-07，
  
  Published：15 June 2025
- 稿件说明：
移动端阅览
张杨,程智宇,陈允降等.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,2025,07(02):268-276.

ZHANG Yang,CHENG Zhiyu,CHEN Yunjiang,et al.Foreign object detection on coal conveyor belt enhanced by attention mechanism[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):268-276.
张杨,程智宇,陈允降等.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,2025,07(02):268-276. DOI： 10.11959/j.issn.2096-6652.202518.

ZHANG Yang,CHENG Zhiyu,CHEN Yunjiang,et al.Foreign object detection on coal conveyor belt enhanced by attention mechanism[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):268-276. DOI： 10.11959/j.issn.2096-6652.202518.

摘要

在电厂煤炭输送这一特殊环境中存在着诸多复杂因素，如光线不均匀、粉尘干扰以及输煤传送带上异物的形状、尺寸、材质各异等。当前众多目标检测算法在这种复杂环境下，对异物特征的敏感度不足，难以有效区分不同特征的异物。为了解决这一问题，对原YOLOv8算法的网络结构进行了优化，提出了YOLOv8-CPCA检测方法。该方法通过引入通道先验卷积注意力（channel prior convolutional attention，CPCA）机制，显著提高了模型的特征提取能力，实现了在电厂煤炭输送的恶劣环境下对异物的高精度检测。CPCA机制通过独特的卷积与池化操作组合，对输入特征图进行全局平均池化与最大池化，深度挖掘多维度特征信息，再经非线性变换精准生成各通道及空间位置的注意力权重，引导模型将注意力高度集中于异物的关键特征区域，强化特征提取能力。实验结果表明，改进后的模型在确保检测实时性的同时，平均检测精度mAP@0.5提高至92.9%，为输煤传送带异物检测提供了更加精准的解决方案，切实保障电厂煤炭输送的安全运行。

Abstract

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.

关键词

Keywords

references

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