基于YOLOv4-tiny的溜筒卸料煤尘检测方法

煤炭港在使用装船机的溜筒卸载煤的过程中会产生扬尘，港口为了除尘，需要先对粉尘进行检测。为解决粉尘检测问题，本文提出一种基于深度学习(YOLOv4-tiny)的溜筒卸料煤粉尘的检测方法。利用改进的YOLOv4-tiny算法对溜筒卸料粉尘数据集进行训练和测试，由于检测算法无法获知粉尘浓度，本文将粉尘分为四类分别进行检测，最后统计四类粉尘的检测框总面积，通过对这些数据做加权和计算近似判断粉尘浓度大小。实验结果表明，四类粉尘的检测精度(AP)分别为93.98%、93.57%、80.03%和57.43%，平均检测精度(mAP)为81.27%，接近YOLOv4的83.38%，而检测速度(FPS)为25.1，高于YOLOv4的13.4。该算法较好地平衡了粉尘检测的速率和精度，可用于实时的粉尘检测以提高抑制溜筒卸料产生的煤粉尘的效率。

The detection method for coal dust caused by chute discharge based on YOLOv4-tiny

The coal port will produce dust in the process of unloading coal by the chute of the ship loader. In order to solve the problem of dust detection, this paper proposes a method of coal dust detection based on deep learning (YOLOv4-tiny). The improved YOLOv4-tiny network is used to train and test the dust data set of chute discharge. Because the detection algorithm cannot get the dust concentration, this paper divides the dust into four categories for detection, and finally counts the area of detection frames of the four categories of dust. After that, the dust concentration is approximately judged through the weighted sum calculation of these data. The experimental results show that the detection accuracy (AP) of four types of dust is 93.98%, 93.57%, 80.03% and 57.43%, the average detection accuracy (mAP) is 81.27% (which is close to 83.38% of YOLOv4), and the detection speed (FPS) is 25.1 (which is higher than 13.4 of YOLOv4). The algorithm can balance the speed and accuracy of dust detection, and can be used for real-time dust detection to improve the efficiency of suppressing coal dust generated by chute discharge.