Binary information enhancement network for efficient steel defects detection and classification

Defect detection plays a crucial role in ensuring the safety and longevity of structures, with defect region classification particularly beneficial for focusing efforts on potential defect areas. Traditional deep convolutional neural networks (DCNNs) based defect classification networks still have a high number of parameters and computational demands, making them unsuitable for embedded systems. This paper proposes the Adaptive Prior Activation-Based Binary Information Enhancement Network (AOIE-Net), which significantly reduces computational requirements by binarizing weights and activations. Specifically designed for steel defect detection, AOIE-Net optimizes the binary quantization process and enhances feature representation to improve the performance of BNNs in steel defect detection tasks. AOIE-Net introduces a Dual Batch Normalization-based Information Enhancement Block (DBN-IEB) and an Adaptive Binary Activation Independent Optimization (ABA-IO) method to reduce computational complexity while boosting classification accuracy. Experimental results demonstrate that AOIE-Net outperforms state-of-the-art binary neural network models on CIFAR-10, ImageNet, and the NEU-CLS steel defect dataset, achieving classification accuracy of 90.6%, 72.1%, and 99.4%, respectively. The proposed method offers an efficient, low-complexity solution for real-time defect classification in large-scale structural inspections and holds significant potential for practical applications.