An image classification algorithm for MSWI based on ResNet and attention mechanism

The municipal solid waste incineration has great advantages for resource recovery and utilization, and the stability of its combustion state is of great significance to the control process. However, the problem of low accuracy still exists in the classification of the images of municipal solid waste incineration (MSWI). For this reason, this paper proposes an image classification algorithm for MSWI based on attention module. The framework of ResNet is employed to improve the depth and accuracy of the network, which solves the problem of insufficient depth of the traditional network while suffering from the deficiency of slow convergence speed. Consequently, the attention module is embedded to solve the problem of different importance of different parts in the incineration flame image. The results show that the algorithm improves the recognition accuracy of municipal solid waste incineration flame images to 96.7%, and the convergence speed is significantly improved compared to other basic neural network models.