# A Lightweight Model for Hot-Rolled Steel Strip Surface Defect Recognition

## TL;DR

This paper introduces a lightweight model for detecting defects on hot-rolled steel strips, suitable for mobile devices and achieving high accuracy.

## Contribution

A novel lightweight model optimized for mobile deployment with high accuracy and reduced computational requirements.

## Key findings

- The model achieves 96.333% accuracy on the NEU-CLS dataset.
- Classification time is reduced by 155.010% compared to the original model.
- The model is successfully deployed on a Raspberry Pi for real-time defect recognition.

## Abstract

With the rapid development of intelligent manufacturing and industrial automation, defect recognition and detection of hot-rolled strip steel have become crucial to ensuring both production efficiency and product quality. However, existing hot-rolled strip steel detection systems often rely on expensive, energy-intensive, stationary equipment, making them unsuitable for mobile applications, such as outdoor use. To address this challenge, this paper proposes and designs a lightweight dual-surface defect recognition model for hot-rolled steel strips that can be implemented on mobile low-power devices (e.g., Raspberry Pi). First, to train the lightweight model, the NEU-CLS dataset is augmented through image generation via StyleGAN3, denoising with a water-wave-like noise removal algorithm, and super-resolution with Real-ESRGAN. Then, MMAM-EfficientNet-B0 is pruned during training, and the Network Slimming algorithm is applied to optimize it on the expanded NEU-CLS dataset, removing 70% of the network structure. Finally, the pruned recognition model is deployed on a Raspberry Pi, achieving an accuracy of 96.333%, with a classification time of 1.527 s per image, a reduction of 155.010% compared to the original model. Our experiments confirm the real-time effectiveness and practical application value of the model.