A Novel Automatic Classification Method Based on the Hybrid Lightweight Shunt Network for Sintered Surfaces

Abstract

Accurate control of sintering temperature is crucial for synthesizing high-quality iron ores. In practice, the temperature is usually regulated by experienced operators through adjusting igniters according to the class of the sintered surface. However, the manual method is difficult to meet the demand for real-time classification in a continuously running sintering process. In this article, to solve the above problem, we design a convolutional neural network with the hybrid lightweight shunt (HLWS-Net) for the online classification of sintered surfaces. Specifically, we introduce a lightweight shunt (LWS) module that guides the network to focus on feature cues at different scales with low computational complexity, avoiding the neglect of vital features in small regions. In addition, to reduce the information loss due to the lightweight structure, the Res-LWS module is proposed by combining the LWS module with the residual connection. Finally, the hybrid use of LWS and Res-LWS improves the classification performance of the network with little additional computational burden. To the best of our knowledge, this is the first method for sintering quality assessment from sintered surface images, which provides a new perspective to automate the sintering process. The experiments demonstrate that the proposed algorithm outperforms state-of-the-art deep learning classification methods with 92.1% classification accuracy, and the entire classification process takes only 0.038 seconds for a single image, which achieves a reasonable accuracy/speed trade-off.