Work

Steel Surface Defect Detection Based on Improved YOLOv8 with Multi-Scale Feature Fusion and Attention Mechanism

Year: 2026

Type: article

Abstract: Identifying microscopic textural anomalies and filtering out complicated industrial background noise remain significant hurdles in inspecting metallic surfaces. To tackle these operational bottlenecks, our research introduces a refined multi-scale detection framework built upon the YOLOv8l architecture. Specifically, we engineer a fine-grained detection pathway utilizing the P2 layer, which aims to preserve critical details of miniature flaws that are otherwise discarded during feature extraction. Furthermore, a Bi-directional Feature Pyramid Network model is embedded to reconstruct the feature fusion path, balancing the preservation of shallow geometric textures with enhanced multi-scale representation capabilities. To bolster anti-interference performance, a Convolutional Block Attention Module (CBAM) is integrated prior to the detection head, employing adaptive channel and spatial weighting to suppress unstructured background noise. Experimental results utilizing TTA demonstrate that the [email protected] reached 76.3%. Detection accuracies for patches and inclusions reached 93.1% and 85.3%.

Source: Electronics

DOI: 10.3390/electronics15071408

Authors: Yalei Jia, Xian Zhang, Jianhui Meng, Jisong Zang

Institutions: North China Institute of Aerospace Engineering, North China Electric Power University, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China