Research on coal and gangue detection algorithm based on improved YOLOv5s model
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摘要: 针对现有基于深度学习的煤矸目标检测方法存在检测速度慢且检测精度较低等问题，提出了一种改进YOLOv5s模型，并将其应用于煤矸目标检测中。改进YOLOv5s模型在YOLOv5s模型Backbone区域嵌入自校正卷积(SCConv)作为特征提取网络，可更好地融合多尺度特征信息；由于煤块和矸石的尺寸相对整张图像过小，对YOLOv5s模型Neck区域进行适当精简，将适合检测较大尺寸对象的19×19特征图分支删除，从而降低模型复杂度并提高检测实时性；对通过K-means算法聚类得到的锚框进行线性缩放，提高模型检测精度。基于改进YOLOv5s模型的煤矸目标检测实验表明，相较于YOLOv5s模型，改进YOLOv5s模型能准确检测出相应的煤块和矸石，且改进YOLOv5s模型大小降低了1.57 MB，帧速率增加了2.1帧/s，平均精度均值提高了1.7%，表明改进YOLOv5s模型检测精度和检测速度均有提升。Abstract: In order to solve the problems of slow detection speed and low detection precision of the existing deep learning-based coal and gangue target detection methods, an improved YOLOv5s model is proposed and applied to coal and gangue target detection.The YOLOv5s model is improved by embedding self-calibrated convolutions(SCConv)in the Backbone area of YOLOv5s model as the characteristic extraction network, which can better fuse multi-scale characteristic information.Because the size of coal and gangue is too small compared with the whole image, the Neck area of YOLOv5s model is appropriately simplified, and the 19×19 characteristic map branches suitable for detecting larger size objects are deleted, thus reducing model complexity and improving the real-time detection performance.The anchor box obtained by clustering with K-means algorithm is linearly scaled to improve the model detection precision.The experiment of coal and gangue target detection based on improved YOLOv5s model shows that compared with YOLOv5s model, the improved YOLOv5s model can detect the corresponding coal and gangue accurately.The size of improved YOLOv5s model is reduced by 1.57 MB, the frame rate is increased by 2.1 frames/s, and the average precision is improved by 1.7%, indicating that the improved YOLOv5s model has improved both detection precision and detection speed.
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