Research on coal quantity detection of belt conveyors based on improved MobileNet
In order to realize fast,accurate,and reliable detection of coal quantity on belt conveyors and improve the reliability and safe-ty of belt conveyors,vision-based non-contact coal quantity detection technology has been widely used.Aiming at the detection task of belt conveyor coal quantity,combined with specific industrial application scenarios,a vision-based coal quantity detection method was designed.The process of image capture,image processing,and image classification was used to predict the coal quantity.Design an im-proved MobileNet classification algorithm for image classification problems,use the space and the channel attention mechanism to sup-press the interference of invalid information,and use the CSP(Cross Stage Partial)structure to improve the expressive ability of the network.To solve the problem of uneven distribution of data labels,the down-sampling method was used to maintain the balance of input picture labels during training.Finally,the comparative experiment was conducted on a real coal quantity image dataset.The results show that the proposed improved algorithm has better performance in terms of accuracy,floating-point operations,and inference time.
万方数据
维普
