Real-time and accurate measurement of coal quantity is the key to energy-saving and speed regulation of belt conveyor. The electronic belt scale and the nuclear scale are the commonly used methods for detecting coal quantity. However, the electronic belt scale uses contact measurement with low measurement accuracy and a large error range. Although nuclear detection methods have high accuracy, they have huge potential safety hazards due to radiation. Due to the above reasons, this paper presents a method of coal quantity detection and classification based on machine vision and deep learning. This method uses an industrial camera to collect the dynamic coal quantity images of the conveyor belt irradiated by the laser transmitter. After preprocessing, skeleton extraction, laser line thinning, disconnection connection, image fusion, and filling, the collected images are processed to obtain coal flow cross-sectional images. According to the cross-sectional area and the belt speed of the belt conveyor, the coal volume per unit time is obtained, and the dynamic coal quantity detection is realized. On this basis, in order to realize the dynamic classification of coal quantity, the coal flow cross-section images corresponding to different coal quantities are divided into coal type images to establish the coal quantity data set. Then, a Dense-VGG network for dynamic coal classification is established by the VGG16 network. After the network training is completed, the dynamic classification performance of the method is verified through the experimental platform. The experimental results show that the classification accuracy reaches 94.34%, and the processing time of a single frame image is 0.270s.

中文翻译：

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基于机器视觉和深度学习的永磁直驱带式输送机煤量动态检测与分类

煤量的实时准确计量是带式输送机节能调速的关键。电子皮带秤和核秤是常用的煤量检测方法。但电子皮带秤采用接触式测量，测量精度低，误差范围大。核探测方法虽然精度高，但由于辐射存在巨大的安全隐患。基于以上原因，本文提出了一种基于机器视觉和深度学习的煤量检测与分类方法。该方法利用工业相机采集激光发射器照射的输送带的动态煤量图像。经过预处理、骨架提取、激光细化、断开连接、图像融合、填充，对采集到的图像进行处理，得到煤流断面图像。根据带式输送机的截面积和带速，得到单位时间的煤量，实现动态煤量检测。在此基础上，为了实现煤量的动态分类，将不同煤量对应的煤流断面图像划分为煤种图像，建立煤量数据集。然后，通过VGG16网络建立了用于动态煤炭分类的Dense-VGG网络。网络训练完成后，通过实验平台验证该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 根据带式输送机的截面积和带速，得到单位时间的煤量，实现动态煤量检测。在此基础上，为了实现煤量的动态分类，将不同煤量对应的煤流断面图像划分为煤种图像，建立煤量数据集。然后，通过VGG16网络建立了用于动态煤炭分类的Dense-VGG网络。网络训练完成后，通过实验平台验证该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 根据带式输送机的截面积和带速，得到单位时间的煤量，实现动态煤量检测。在此基础上，为了实现煤量的动态分类，将不同煤量对应的煤流断面图像划分为煤种图像，建立煤量数据集。然后，通过VGG16网络建立了用于动态煤炭分类的Dense-VGG网络。网络训练完成后，通过实验平台验证该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 获得单位时间煤量，实现动态煤量检测。在此基础上，为了实现煤量的动态分类，将不同煤量对应的煤流断面图像划分为煤种图像，建立煤量数据集。然后，通过VGG16网络建立了用于动态煤炭分类的Dense-VGG网络。网络训练完成后，通过实验平台验证该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 获得单位时间煤量，实现动态煤量检测。在此基础上，为了实现煤量的动态分类，将不同煤量对应的煤流断面图像划分为煤种图像，建立煤量数据集。然后，通过VGG16网络建立了用于动态煤炭分类的Dense-VGG网络。网络训练完成后，通过实验平台验证该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 将不同煤量对应的煤流断面图像划分为煤种图像，建立煤量数据集。然后，通过VGG16网络建立了用于动态煤炭分类的Dense-VGG网络。网络训练完成后，通过实验平台验证该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 将不同煤量对应的煤流断面图像划分为煤种图像，建立煤量数据集。然后，通过VGG16网络建立了用于动态煤炭分类的Dense-VGG网络。网络训练完成后，通过实验平台验证该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 通过实验平台验证了该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270 通过实验平台验证了该方法的动态分类性能。实验结果表明，分类准确率达到94.34%，单帧图像处理时间为0.270s。