MAG SMART LEMON Recognizing and distinguishing coal and gangue are essential in engineering, such as in coal-fired power plants.This paper employed a convolutional neural network (CNN) to recognize coal and gangue images and help segregate coal and gangue.A typical workflow for CNN image recognition is presented as well as a strategy for updating the model parameters.
Based on a powerful trained image recognition model, VGG16, the idea of transfer learning was introduced to build a custom CNN model to solve the problems of massive trainable parameters and limited computing power linked to the building of a brand-new model from scratch.Two hundred and forty coal and gangue images were collected in a database, including 100 training images and 20 validation images for each material.A recognition accuracy of 82.
5% was obtained for the validation images, which demonstrated a decent performance of our model.According to the analysis of parameter updating in the training process, a principal constraint for obtaining a higher recognition accuracy mainly resided in a shortage of training samples.This model was also used to identify photos from a washing plant stockpiles, which verified its capability of dealing with field pictures.
CNN combined with the transfer learning method we used can provide fast and robust coal/gangue distinction that does not require harsh data support and equipment support.This method Exercise Equipment - Resistance Bands will exhibit brighter prospects in engineering if the target image database (as with the coal and gangue images in this study) can be further enlarged.