Research On Coal Gangue Recognition Algorithm Based On Image

Coal can continuously provide power for industrial production.However,it is often accompanied by gangue,which pollutes the environment.Therefore,the recognition of coal gangue is of great significance.This paper proposes image-based coal gangue recognition algorithms.The main contents of the study are as follows:(1)Build a coal gangue recognition model based on the idea of transfer learning.First,the VGG16 convolution base is used to extract coal gangue image features,combined with three machine learning algorithms to verify the effectiveness of the features.Then,the migration of VGG16 is realized through feature extraction and model fine-tuning respectively,and a custom classifier is constructed to form two recognition models.The simulation results show that the accuracy rates of the two models are 96.30%and 98.15%respectively.(2)Build a lightweight coal gangue recognition model based on YOLOv5s.This paper uses lightweight networks to modify YOLOv5s network to make the model light for CPU and GPU devices respectively;and introduces a convolution attention mechanism,improves the feature extraction capabilities of model.Compared with the original network,the number of parameters of the improved models is reduced by 54.66%,75.62%and 44.46%,and the amount of calculation is reduced by 62.58%,73%and 38%respectively.The results show that the improved models greatly reduce the amount of model parameters and calculation,improve the real-time detection,and provide a reference for solving the problem of complex multi-object coal gangue image detection algorithm and large amount of calculation.