Foreign object detection of coal mine underground conveyor belt based on Stair-YOLOv7-tiny

Abstract

The existing methods for detecting foreign objects in underground coal mine conveyor belts have poor adaptability to complex scenarios, cannot meet real-time and lightweight requirements, and perform poorly when dealing with foreign objects with large size differences. In order to solve the above problems, a Stair-YOLOv7-tiny model is proposed based on the lightweight YOLOv7-tiny model for improvement, and applied to the detection of foreign objects in coal mine underground conveyor belts. This model adds feature concatenation units to the efficient layer aggregation network (ELAN) module to form a Stair-ELAN module. The model fuses low dimensional features from different levels with high-dimensional features, strengthens the direct connection between feature levels, enhances information capture capabilities, and strengthens the model’s adaptability to objects of different scales and complex scenes. The introduction of Stair-head feature fusion (Stair-fusion) for detection heads forms a Stair-head module. The model enhances the feature expression capability of medium and low resolution detection heads by fusing detection head features of different resolutions layer by layer, achieving complementary feature information. The experimental results show that the Stair-YOLOv7 tiny model has better detection performance than CBAM-YOLOv5, YOLOv7 tiny, and its lightweight model on the open-source dataset CUMT BelT for conveyor belt foreign objects. The accuracy, average precision, recall, and precision are 98.5%, 81.0%, 82.2%, and 88.4%, respectively, and the detection speed is 192.3 frames per second. In the video analysis of conveyor belt monitoring in a certain mine, the Stair-YOLOv7-tiny model does not have any missed or false detection, achieving accurate detection of foreign objects in the conveyor belt.

Abstract

摘要：针对现有煤矿井下输送带异物检测方法应对复杂场景适应性差、无法满足实时性和轻量化要求、处理 尺寸差异较大异物时表现不佳的问题, 基于轻量化YOLOv7-tiny模型进行改进, 提出了一种Stair-YOLOv7-tiny模型, 并将其用于煤矿井下输送带异物检测。该模型在高效层聚合网络(ELAN)模块中添加特征拼接单元, 形成阶梯ELAN(Stair-ELAN)模块, 将不同层级的低维特征与高维特征进行融合, 加强了特征层级间的直接联 系, 提升了信息捕获能力, 增强了模型对不同尺度目标和复杂场景的适应性; 针对检测头引入阶梯特征融合 (Stair-fusion), 形成阶梯检测头(Stair-head)模块, 通过逐层融合不同分辨率的检测头特征, 增强了中低分辨率检 测头的特征表达能力, 实现了特征信息的互补。实验结果表明：Stair-YOLOv7-tiny模型在输送带异物开源数据 集CUMT-BelT上的检测效果优于CBAM-YOLOv5, YOLOv7-tiny及其轻量化模型, 准确率、平均精度均值、召 回率和精确率分别达98.5%, 81.0%, 82.2%和88.4%, 检测速度为192.3帧/s;在某矿井下输送带监控视频分析中, Stair-YOLOv7-tiny模型未出现漏检或误检, 实现了输送带异物的准确检测。