Work

AEB-YOLO: An efficient multi-scale defect detection algorithm for copper strips

Year: 2025

Type: article

Abstract: To address the challenges of low efficiency in detecting surface defects on copper strips in industrial settings, inadequate recognition of small targets, and excessive model parameters, this paper proposes a lightweight algorithm, AEB-YOLOv8n. First, an efficient multi-scale bottleneck module (C2f-EB) is designed to more effectively integrate global and local semantic information via a dual-branch mixed pooling architecture, enhancing multi-scale defect feature extraction in copper strips, significantly improving detection accuracy, and reducing model parameters. Second, an interactive collaborative attention module is proposed to enhance the feature representation of small-target defects by focusing on their spatial locations, suppressing complex background interference, and substantially improving the ability to capture micro-defects. Finally, the bidirectional feature pyramid network structure is incorporated into the neck network to construct a pruned bidirectional feature pyramid with learnable fusion weights, which reduces redundant computations and enhances feature fusion efficiency. Experimental results demonstrate that the improved AEB-YOLOv8n model achieves a 4.6% improvement in [email protected] compared with the original YOLOv8 model while reducing the parameter count by 37.8% and lowering GFLOPs from 8.1 to 7.2, thus meeting the requirements for high accuracy and efficiency in copper strip defect detection.

Source: AIP Advances

DOI: 10.1063/5.0285324

Authors: Bo Zhou, Hu Chen, Jianqing Luo, Peizheng Li, Binbin Xiang, Kai Li

Institutions: Xinjiang University, Tebian Electric Apparatus (China)

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,350