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A Cross-Scale Spatial-Semantic Feature Aggregation Network for Strip Steel Surface Defect Detection.

Chenglong Xu¹, Yange Sun¹, Linlin Huang¹

¹School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

Materials (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

This study introduces a new network for strip steel surface defect detection, improving accuracy for diverse and unevenly distributed defects. The method enhances feature representation and focuses on defect-prone areas for better detection.

Keywords:

adaptive RPN attention mechanism feature aggregation surface defect detection

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Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

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Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Strip steel surface defect detection is challenging due to varied defect scales and spatial distributions.
Existing methods struggle with incomplete feature representation and missed detections in sparse defect regions.

Purpose of the Study:

To propose a novel network for accurate and robust strip steel surface defect detection.
To address challenges of diverse defect scales and uneven spatial distribution.

Main Methods:

Developed a cross-scale spatial-semantic feature aggregation network (CSSFAN).
Employed bottom-up feature aggregation with cross-scale spatial-semantic aggregation modules (CSSAMs).
Introduced an adaptive region proposal network (ARPN) to handle uneven defect distribution.

Main Results:

CSSFAN effectively fuses features across multiple scales, capturing subtle and irregular defect patterns.
ARPN dynamically adjusts region proposals, enhancing sensitivity to defect-prone areas.
Experiments showed significant improvements in detection performance on strip steel datasets.

Conclusions:

The proposed CSSFAN with ARPN offers an effective and robust solution for strip steel surface defect detection.
The method successfully bridges spatial precision and semantic abstraction for improved defect identification.