Coal quantity detection method of belt conveyor based on vision technology
The belt conveyor is widely used in the coal industry.In order to ensure the continuous and stable operation of the equipment,the belt conveyor must be equipped with a corresponding fault detection device.The traditional detection device has a large maintenance workload with a high protection malfunction rate.In this paper,the method of YOLOv5sis improved to detect the coal quantity of the belt conveyor.First,the original backbone network of YOLOv5s is replaced by Mobile-NetV2,then the attention module is introduced after the backbone network,and the BiFPN structure is introduced into the neck.Finally,the model parameters are fine tuned by the method of migration learning.Through the analysis of the experi-mental results,it can be seen that the detection speed of the improved YOLOv5s is 32 FPS and the mAP50 is 98.9％.Com-pared with the common target detection algorithms,it has higher detection accuracy and speed.The improved method can quickly and accurately realize the detection of"no coal quantity","small coal quantity","medium coal quantity","large coal quantity"of the conveyor,thus providing reference for the real-time speed regulation of the conveyor and the design of intelligent video monitoring system.
国家科技期刊平台
NSTL
万方数据
维普
