CCTEG Chongqing Research Institute
State Key Laboratory of Coal Mine Disaster Prevention and Control

针对传统的基于机器视觉的带式输送机输送带撕裂检测算法需要高算力、高功耗AI模组,本安电源无法满足其用电需求的问题,提出一种基于一维双重注意力网络(DANet-1D)的输送带纵向撕裂检测算法。通过工业相机采集输送带表面线激光形成的图像;设计激光条纹特征滤波器,提取条纹特征;设计基于一维双重注意力网络的撕裂检测算法,将撕裂的二维图像数据降维,在一维空间进行神经网络检测,运行速度更快且支持高分辨率图像;研制本安型输送带撕裂检测装置,并进行验证。结果表明:该算法的准确率P为92.54%,召回率R为91.78%,每帧平均检测时间为12.40ms。工业性试验成功检测出输送带模拟撕裂,为输送带纵向撕裂提供了一种新的检测方案。

In view of the problem that the traditional machine vision based conveyor belt tear detection algorithm requires high computing power and high power consumption AI module,and the intrinsic safety power supply can not meet its electrical needs,a longitudinal tear detection algorithm of conveyor belt based on one-dimensional dual attention network(DANet-1D) was proposed. The image of conveyor belt surface line laser was collected by industrial camera. The laser stripe feature filter was designed to extract stripe features. A tear detection algorithm based on DANet-1D was designed to reduce the dimensionality of the torn two - dimensional image data. The detection of neural network operated in one - dimensional space, run faster and supported high-resolution images. An intrinsic safe conveyor belt tear detection device was developed and verified. The results show that the accuracy P of the algorithm is 92. 54%,and the recall rate R is 91. 78%. The average detection time per frame is 12. 40 ms. The industrial test successfully detected simulated tearing of the conveyor belt,providing a new detection scheme for longitudinal tear of the conveyor belt.

中煤科工集团重庆研究院自立重点研发科研项目(2023ZDYF01)

向兆军,游磊,罗明华.基于一维双重注意力网络的输送带纵向撕裂检测算法[J].矿业安全与环保,2024,51(5):89-95.

XIANG Zhaojun,YOU Lei,LUO Minghua. Longitudinal tear detection algorithm of conveyor belt based on DANet-1D[J].Mining Safety & Environmental Protection,2024,51(5):89-95.