# MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects

Source: [MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects](<https://www.sota2.com/research/paper/68fa73571e209131cea5bef7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:48:16.237Z

Authors: Kunye Shen, Xiaofei Zhou, Zhi Liu

Published: 2024-05-25T07:09:11.000Z

## Abstract

The automated surface defect detection is a fundamental task in industrial production, and the existing saliencybased works overcome the challenging scenes and give promising detection results. However, the cutting-edge efforts often suffer from large parameter size, heavy computational cost, and slow inference speed, which heavily limits the practical applications. To this end, we devise a multi-scale interactive (MI) module, which employs depthwise convolution (DWConv) and pointwise convolution (PWConv) to independently extract and interactively fuse features of different scales, respectively. Particularly, the MI module can provide satisfactory characterization for defect regions with fewer parameters. Embarking on this module, we propose a lightweight Multi-scale Interactive Network (MINet) to conduct real-time salient object detection of strip steel surface defects. Comprehensive experimental results on SD-Saliency-900 dataset, which contains three kinds of strip steel surface defect detection images (i.e., inclusion, patches, and scratches), demonstrate that the proposed MINet presents comparable detection accuracy with the state-of-the-art methods while running at a GPU speed of 721FPS and a CPU speed of 6.3FPS for 368\*368 images with only 0.28M parameters. The code is available at https://github.com/Kunye-Shen/MINet.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.16096>)

[PDF](<https://arxiv.org/pdf/2405.16096.pdf>)