在矿山复杂环境中，高磁性矿输送带异物检测面临场景干扰严重、识别难度大的挑战，针对高磁性矿中异物边缘信息易丢失和实时响应难度大的问题，提出了基于YOLOV8的深度学习图像检测方法。首先，建立输送带异物数据集，采用暗通道去雾技术对数据进行预处理，提升图像清晰度；然后，结合YOLOV8的网络特性，引入动态注意力机制，并用蛇形卷积代替普通卷积，允许模型在处理输入数据时动态地分配注意力，同时捕捉到更广泛的局部和全局特征；最后，改进动态检测头来灵活适配多尺度和多方向的检测需求，以提升模型的适应性并降低参数计算量。试验结果表明：基于YOLOV8的异物检测模型平均检测准确率达到96.4%，召回率为91%，平均检测时间仅为29 ms，完全满足矿山皮带运输现场对精准检测和实时性的要求。

In the intricate setting of mining operations，identifying foreign objects on conveyor belts transporting high-magnetic ore is hindered by significant scene interference and substantial recognition challenges.To address the issues of frequent loss of foreign object edge information and the considerable difficulty in achieving real-time，high-speed responses in high-magnetic environments，we propose an image recognition and detection methodology grounded in deep learning techniques.Initially，a dataset of foreign objects on conveyor belts is constructed.To address the issue of image blurring，which arises from the high-speed operation of the belt conveyor and the limited data acquisition frequency of industrial cameras，the dark channel defogging technique is employed to preprocess the data，thereby enhancing image clarity.Subsequently，the core architecture of YOLOV8 is refined by incorporating a dynamic attention mechanism and substituting standard convolution with snake convolution.The dynamic attention mechanism enables the model to dynamically allocate focus during input data processing.Concurrently，the integration of snake convolution in place of traditional convolution，in conjunction with C2f，significantly enhances the model’s capacity to process image details.This unique structure facilitates the capture of a broader spectrum of local and global features，thereby substantially reducing the model’s rates of false positives and missed detections concerning buried foreign objects.In conclusion，the YOLOV8 architecture has been enhanced through the integration of a dynamic detection head，which allows for flexible adaptation to multi-scale and multi-directional detection requirements.This modification aims to improve the model’s adaptability and optimize the reduction of computational parameters，thereby significantly enhancing its real-time performance in complex environments.Experimental results demonstrate that the model achieves an average detection accuracy of 96.4%，a recall rate of 91%，and an average detection time of merely 29 milliseconds.The algorithm presented in this paper de-monstrates an enhancement in average detection accuracy and recall by 5.2% and 6.2%，respectively，compared to the original network，thereby confirming its efficacy.This improved algorithm adequately satisfies the demands for precise detection and real-time performance in the context of mine belt transportation，offering substantial support for advancing mine safety management and operational efficiency.

中国有色金属报）

http://www.goldsci.ac.cn/article/2025/1005-2518/1005-2518-2025-33-1-193.shtml