注意力机制增强的输煤传送带异物检测

张杨; 程智宇; 陈允降; 张建南; 袁文胜; 张慧

doi:10.11959/j.issn.2096-6652.202518

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注意力机制增强的输煤传送带异物检测

学术论文 | 更新时间：2025-07-21

- 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
- 智能科学与技术学报 2025年7卷第2期页码：268-276
- 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：
  
  [ "张杨（1988- ），男，国家电投集团协滨海发电有限公司经济师，主要研究方向为燃煤发电厂煤炭接卸、存储、掺配、输送、盘点管理。" ]
  [ "程智宇（2003- ），男，北京交通大学计算机科学与技术学院硕士生，主要研究方向为深度学习、计算机视觉。" ]
  [ "陈允降（1982- ），男，国家电投集团协滨海发电有限公司工程师，主要研究方向为火力发电厂生产运营管理。" ]
  [ "张建南（1989- ），男，国家电投集团协滨海发电有限公司高级工程师，主要研究方向为火力发电厂生产运营管理。" ]
  [ "袁文胜（1984- ），男，国家电投集团协滨海发电有限公司工程师，主要研究方向为火力发电厂热控设备自动化。" ]
  [ "张慧（1993- ），女，北京交通大学计算机科学与技术学院副教授，主要研究方向为复杂环境下的车辆感知、多传感器融合的协同检测、多模态目标检测、群体智能、平行视觉。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202518
  中图分类号： TP391
- 收稿：2025-02-19，
  
  修回：2025-05-07，
  
  纸质出版：2025-06-15
- 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

吴问友. 基于电厂输煤系统皮带机常见故障分析[J]. 水利电力技术与应用, 2025, 7: 169-171.

WU W Y. Analysis of typical faults in conveyor belts of power plant coal handling systems[J]. Water Science and Engineering, 2025, 7: 169-171.

REDMON J, FARHADI A. YOLOv3: an incremental improvement[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 89-95.

VARGHESE R, M S. YOLOv8: a novel object detection algorithm with enhanced performance and robustness[C]//Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). Piscataway: IEEE Press, 2024: 1-6.

HUANG H J, CHEN Z G, ZOU Y, et al. Channel prior convolutional attention for medical image segmentation[J]. Computers in Biology and Medicine, 2024, 178: 108784.

ZHOU D H, ZHAO Y H, WANG Z D, et al. Review on diagnosis techniques for intermittent faults in dynamic systems[J]. IEEE Transactions on Industrial Electronics, 2020, 67(3): 2337-2347.

ZHANG H, LUO G Y, LI Y D, et al. Parallel vision for intelligent transportation systems in metaverse: challenges, solutions, and potential applications[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(6): 3400-3413.

SKVRNA J, NEUMANN L. TCC-det: temporarily consistent cues for Weakly-supervised 3D detection[C]//Proceedings of the 2024 European Conference on Computer Vision (ECCV). Cham: Springer Nature Switzerland, 2025: 129-145.

PAPAGEORGIOU C P, OREN M, POGGIO T. A general framework for object detection[C]//Proceedings of the Sixth International Conference on Computer Vision. Piscataway: IEEE Press, 1998: 555-562.

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Piscataway: IEEE Press, 2005: 886-893.

CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.

周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.

ZHOU F Y, JIN L P, DONG J. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251.

王彦雅. 基于Two-Stage的目标检测算法综述[J]. 河北省科学院学报, 2022, 39(2): 14-22.

WANG Y Y. Overview of target detection algorithms based on two stage[J]. Journal of the Hebei Academy of Sciences, 2022, 39(2): 14-22.

刘俊明, 孟卫华. 基于深度学习的单阶段目标检测算法研究综述[J]. 航空兵器, 2020, 27(3): 44-53.

LIU J M, MENG W H. Review on single-stage object detection algorithm based on deep learning[J]. Aero Weaponry, 2020, 27(3): 44-53.

REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

ZHANG H, TIAN Y L, WANG K F, et al. Mask SSD: an effective single-stage approach to object instance segmentation[J]. IEEE Transactions on Image Processing, 2019, 29: 2078-2093.

ZHANG H, LUO G Y, WANG X, et al. SASAN: shape-adaptive set abstraction network for point-voxel 3D object detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(2): 2465-2479.

CAI X H, LAI Q X, WANG Y W, et al. Poly kernel inception network for remote sensing detection[C]//Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2024: 27706-27716.

ZHANG S H, QU C N, RU C Y, et al. Multi-objects recognition and self-explosion defect detection method for insulators based on lightweight GhostNet-YOLOv4 model deployed onboard UAV[J]. IEEE Access, 2023, 11: 39713-39725.

田庆, 胡蓉, 李佐勇, 等. 基于SE-YOLOv5s的绝缘子检测[J]. 智能科学与技术学报, 2021(3): 312-321.

TIAN Q, HU R, LI Z Y, et al. Insulator detection based on SE-YOLOv5s[J]. Chinese Journal of Intelligent Science and Technology, 2021(3): 312-321.

贾尚锋, 王志强, 郝鑫宇. 基于小样本的传送带异物视频检测算法[J]. 中国安全科学学报, 2023, 33(S1): 134-137.

JIA S F, WANG Z Q, HAO X Y. Video detection algorithm of foreign bodies in conveyor belt based on small samples[J]. China Safety Science Journal, 2023, 33(S1): 134-137.

高涵, 赵培培, 于正, 等. 基于特征增强与Transformer的煤矿输送带异物检测[J]. 煤炭科学技术, 2024, 52(7): 199-208.

GAO H, ZHAO P P, YU Z, et al. Coal mine conveyor belt foreign object detection based on feature enhancement and Transformer[J]. Coal Science and Technology, 2024, 52(7): 199-208.

WU Y, CHEN Y P, YUAN L, et al. Rethinking classification and localization for object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 10186-10195.

YANG Z, LIU S H, HU H, et al. RepPoints: point set representation for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2019, 9656-9665.

杨锁荣, 杨洪朝, 申富饶, 等. 面向深度学习的图像数据增强综述[J]. 软件学报, 2024, 45(1): 1-19.

YANG S R, YANG H C, SHEN F R, et al. A survey on image data augmentation for deep learning[J]. Journal of Software, 2024, 45(1): 1-19.

ZHAO Y A, LYU W Y, XU S L, et al. DETRs beat YOLOs on real-time object detection[C]//Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2024: 16965-16974.

LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 2016 European Conference on Computer Vision (ECCV). Cham: Springer International Publishing, 2016: 21-37.

WU Y, CHEN Y, YUAN L, et al. Global attention mechanism: retain information to enhance channel-spatial interactions[J]. IEEE Transactions on Image Processing, 2021, 30: 2969-2982.

VASWANI A. Attention is all you need[J ] . Advances in Neural Information Processing Systems, 2017, 30 .

MISRA D, NALAMADA T, ARASANIPALAI A U, et al. Rotate to attend: convolutional triplet attention module[C]//Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway: IEEE Press, 2021: 3138-3147.

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