BDR-Net: Digital Recognition Network for Billet Surface Based on Flow Alignment and Attention Mechanism

Abstract The surface characteristics of billets are crucial for subsequent traceability, yet the production process generates intricate digital features on their surfaces. This paper introduces BDR-Net, a novel billet surface digit recognition network. Drawing inspiration from Inception, the network adopts a ResNext-like architecture as its primary framework. It uniformly distributes output in dimension, extracts positional and scale features separately, and introduces a mixed dilated convolution block to reduce parameters while expanding the sensory field. To address the challenge of lost up-sampled features during fusion, an innovative stream alignment-based up-sampled feature fusion algorithm is proposed. Additionally, to enhance the network's focus on extracting salient spatial and channel features, a mixed-dimensional attention mechanism (scSE) is integrated into the alignment-based upsampling feature fusion module. Experimental results showcase BDR-Net's outstanding performance, achieving an impressive 95.6\% accuracy in digitally classifying billet surfaces, surpassing the ResNext50\_32x4d benchmark model by 4.3\% in recognition accuracy. Moreover, compared to current classification networks, this model exhibits significant accuracy improvements. Furthermore, the [email protected] metric reaches 0.897, surpassing current classification networks. These findings underscore the remarkable performance of the model in billet surface digit recognition, offering an effective solution for digit recognition on billet surfaces in steel mills.