Adaptive Sampling and Ghost Multi-scale Fusion for Lightweight Weld Defect Detection

Authors

Bin Lu, Department of Computer, North China Electric Power University, Baoding 071003 China; Hebei Key Laboratory of Knowledge Computing for Energy & Power, North China Electric Power University, Baoding 071003, China
Xuan Yang, Department of Computer, North China Electric Power University, Baoding 071003 China; Hebei Key Laboratory of Knowledge Computing for Energy & Power, North China Electric Power University, Baoding 071003, China
Zhenyu Yang, Department of Computer, North China Electric Power University, Baoding 071003 China; Hebei Key Laboratory of Knowledge Computing for Energy & Power, North China Electric Power University, Baoding 071003, China
Xiaotian Gao, Department of Computer, North China Electric Power University, Baoding 071003 China

Abstract

Abstract: To improve the accuracy and speed of welding defect detection and achieve lightweight models, a lightweight weld defect detection network based on YOLOv8, named light adaptive-weight sampling-YOLO (LAW-YOLO), was proposed. A lightweight adaptive weight sampling LAWS module was designed. It constructed an adaptive weight attention feature map by learning the interacting features within the receptive field. An optimized efficient weighted bidirectional feature pyramid network was adopted as the feature extraction backbone in LAW-YOLO. Furthermore, a ghost multi-scale sampling module was designed, and a hybrid attention mechanism was introduced to enhance the detection capability for small-scale defect targets. Experimental results demonstrate that the average precision (mAP0.5) of the proposed approach on the SteelTube dataset reaches 97.6%, with a data processing speed of 91 frames per second. The approach achieves a 5.5% increase in average defect recognition accuracy and a 4.6% improvement in processing speed compared to the original model and maintains high efficiency while reducing computation by 25.3% and model size by 50%, facilitating deployment on edge devices for operational tasks.

Recommended Citation

Lu, Bin; Yang, Xuan; Yang, Zhenyu; and Gao, Xiaotian (2025) "Adaptive Sampling and Ghost Multi-scale Fusion for Lightweight Weld Defect Detection," Journal of System Simulation: Vol. 37: Iss. 8, Article 7.
DOI: 10.16182/j.issn1004731x.joss.24-0244
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss8/7

First Page

1978

Last Page

1990

CLC

TP391.41

Recommended Citation

Lu Bin, Yang Xuan, Yang Zhenyu, et al. Adaptive Sampling and Ghost Multi-scale Fusion for Lightweight Weld Defect Detection[J]. Journal of System Simulation, 2025, 37(8): 1978-1990.

Corresponding Author

Yang Xuan

DOI

10.16182/j.issn1004731x.joss.24-0244