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面向筛面复杂背景的矿山异物视觉检测方法

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矿山异物检测是异物智能化去除的前提,更是保障设备安全运行,矿山正常生产的关键。在矿山生产过程中,异物来源广泛,种类繁杂。针对传统的矿山异物检测方法面临适应性差和效率低的问题,提出了一种面向大型振动筛筛面的矿山异物检测算法模型。为解决强振动、矿石遮挡和粉尘水雾等复杂环境的干扰,该模型引入了改进的显式视觉中心模块(EVCBlock),轻量化上采样算子CARAFE和基于动态非单调聚焦机制的梯度增益损失函数WiseIoU-v3,有效提升了在复杂环境下的异物检测性能。利用TensorRT对模型优化并部署至边缘计算设备Jetson Xavier NX,实现了在边缘侧的异物检测。研究结果表明:该模型在振动筛筛面异物检测上的表现明显好于其他对比模型。经多线程视频推流测试,模型部署至边缘计算设备平均识别精确率可以达到96。3%,平均帧率达到25 FPS以上,满足了实际检测要求。
A Visual Detection Method of Foreign Bodies in Mines Facing Complex Background of Screen Surface
Foreign bodies detection in mines is the premise of intelligent removal of foreign bodies,but also the key to ensure the safe op-eration of equipment and the normal production of the diggings.Foreign bodies come from a wide range of sources and various types in the process of mines production.Aiming at the problems of poor adaptability and low efficiency of traditional foreign bodies detection methods,a foreign bodies detection algorithm model for surface of large vibrating screen was proposed.In order to solve the interference of complex environment such as violent vibration,shielding of ore,dust and water mist,the improved Explicit Vision Center block(EVCBlock),lightweight up-sampling operator CARAFE and gradient gain loss function WiseIoU-v3 based on dynamic non-monotony focusing mechanism are introduced in this model,which effectively improve the detection performance of foreign bodies in complex envi-ronment.The model with TensorRT optimization was deployed to the edge computing device Jetson Xavier NX to achieve foreign object detection on the edge side.The results show that the proposed model is better than other models in detecting foreign bodies on the vibrating screen surface.After multithreaded video push streaming test,the average accuracy of deploying to the edge computing device can reach96.3%,and the average frame rate can reach more than25 FPS,which meets the actual detection requirements.

foreign bodies detection in minesvibrating screenviolent interferenceAntijam-YOLOedge computing

刘善明、余新阳、欧阳魁

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江西省矿业工程重点实验室,江西 赣州 341000

江西理工大学 资源与环境工程学院,江西 赣州 341000

湖南领头雁矿业科技有限责任公司,湖南 长沙 410000

矿山异物检测 振动筛 强干扰 Antijam-YOLO 边缘计算

国家自然科学基金

52264023

2024

计算机技术与发展
陕西省计算机学会

计算机技术与发展

CSTPCD
影响因子:0.621
ISSN:1673-629X
年,卷(期):2024.34(5)
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