# MobileSteelNet: A Lightweight Steel Surface Defect Classification Network with Cross-Interactive Efficient Multi-Scale Attention

## TL;DR

MobileSteelNet is a lightweight deep learning model that improves steel surface defect classification accuracy while being efficient enough for real-time industrial use.

## Contribution

The paper introduces MobileSteelNet with novel modules MSFF and CIEMA for efficient multi-scale attention and feature fusion in lightweight networks.

## Key findings

- MobileSteelNet achieves 91.36% average accuracy on the NEU-DET dataset, outperforming ResNet-50 and MobileNetV2.
- It achieves 93.70% accuracy on Scratch-type defects, an 82.12 percentage point improvement over MobileNetV1.
- The model size is only 8.2 MB, making it suitable for edge deployment in industrial vision systems.

## Abstract

Steel surface defect classification is critical for industrial quality control, yet existing methods struggle to balance accuracy and efficiency for real-time deployment in vision-based sensor systems. This paper presents MobileSteelNet, a lightweight deep learning framework that introduces two novel modules: multi-scale feature fusion (MSFF), for integrating multi-stage features; and Cross-Interactive Efficient Multi-Scale Attention (CIEMA), which unifies inter-channel interaction, parallel multi-scale spatial extraction, and grouped efficient computation. Experiments on the NEU-DET dataset demonstrate that MobileSteelNet achieves 91.36% average accuracy, surpassing ResNet-50 (88.01%) and lightweight networks, including MobileNetV2 (86.08%). Notably, it achieves 93.70% accuracy on Scratch-type defects, representing an 82.12 percentage point improvement over baseline MobileNetV1. With a model size of only 8.2 MB, MobileSteelNet maintains superior performance while meeting lightweight deployment requirements, making it suitable for edge deployment in vision sensor systems for steel manufacturing.