Improved weld surface defect detection algorithm from YOLOv8

Zhang Runmei; Pan Chenfei; Chen Zihua; Chen Zhong; Yuan Bin

doi:10.12086/oee.2025.240296

Article navigation > Opto-Electronic Engineering > 2025 Vol. 52 > No. 3 > 240296

Next Article Previous Article

Zhang R M, Pan C F, Chen Z H, et al. Improved weld surface defect detection algorithm from YOLOv8[J]. Opto-Electron Eng, 2025, 52(3): 240296. doi: 10.12086/oee.2025.240296

Citation:

Zhang R M, Pan C F, Chen Z H, et al. Improved weld surface defect detection algorithm from YOLOv8[J]. Opto-Electron Eng, 2025, 52(3): 240296. doi: 10.12086/oee.2025.240296

Improved weld surface defect detection algorithm from YOLOv8

1.
School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, Anhui 230601, China
2.
School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui 230601, China

Fund Project: Anhui Provincial Natural Science Projects in Universities (2024AH050234), Open Research Project of Anhui Simulation Design and Modern Manufacture Engineering Technology Research Center (SGCZXZD2101), Provincial and Ministerial Key Laboratory of Chang'an University (300102254501-202412), and Anhui University Natural Science Key Research Project (2024AH050245)

More Information

^*Corresponding author: czh1619@ahjzu.edu.cn
CSTR: 32245.14.oee.2025.240296

Received Date 17 December 2024

Revised Date 24 February 2025

Accepted Date 25 February 2025

Published Date 28 March 2025

Abstract

Abstract

In response to the problems of traditional defect detection algorithms, such as poor accuracy and feature loss in practical applications due to the inconspicuous characteristics of welding defects and complex background information, this paper proposes a welding surface defect detection algorithm based on the improved YOLOv8 (GD-YOLO). The model first introduces the fusion of feature extraction modules and convolutional modules to enhance its information extraction capabilities. Then, a slim-neck structure is embedded in the neck network, and the upsampling operator CAFARE is referenced in the feature fusion stage to assist in enhancing the model's performance. Subsequently, the attention mechanism module is improved to optimize the overall performance without significantly increasing the computational burden. Finally, the loss function is changed to Inner-SIOU to address the problem of mismatched bounding boxes. Experimental results show that the mAP0.5 detection metric of the model in this paper is 7.8% higher than that of the baseline model, and the number of parameters and the amount of computation are reduced by 0.2 M and 0.7 G, respectively.
- deep learning /
- defect detection /
- YOLOv8 /
- Inner-SIoU

FullText(HTML)

References

[1]	梁礼明, 龙鹏威, 卢宝贺, 等. 改进GBS-YOLOv7t的钢材表面缺陷检测[J]. 光电工程, 2024, 51(5): 240044. doi: 10.12086/oee.2024.240044 CrossRef Google Scholar Liang L M, Long P W, Lu B H, et al. Improvement of GBS-YOLOv7t for steel surface defect detection[J]. Opto-Electron Eng, 2024, 51(5): 240044. doi: 10.12086/oee.2024.240044 CrossRef Google Scholar
[2]	苏虎, 张家斌, 张博豪, 等. 基于视觉感知的表面缺陷检测综述[J]. 计算机集成制造系统, 2023, 29(1): 169−191. doi: 10.13196/j.cims.2023.01.015 CrossRef Google Scholar Su H, Zhang J B, Zhang B H, et al. Review of surface defect inspection based on visual perception[J]. Comput Integr Manuf Syst, 2023, 29(1): 169−191. doi: 10.13196/j.cims.2023.01.015 CrossRef Google Scholar
[3]	阳丽莎, 李茂军, 胡建文, 等. 基于改进YOLOv7-tiny的带钢表面缺陷检测算法[J]. 计算机工程, 2025, 51(1): 208−215. doi: 10.19678/j.issn.1000-3428.0068397 CrossRef Google Scholar Yang L S, Li M J, Hu J W, et al. Strip steel surface defect detection algorithm based on improved YOLOv7-tiny[J]. Comput Eng, 2025, 51(1): 208−215. doi: 10.19678/j.issn.1000-3428.0068397 CrossRef Google Scholar
[4]	Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, Amsterdam, 2016: 21–37. https://doi.org/10.1007/978-3-319-46448-0_2. Google Scholar
[5]	Redmon J. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779–788. https://doi.org/10.1109/CVPR.2016.91. Google Scholar
[6]	Wang C, Bochkovskiy A, Liao H M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of 2013 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721. Google Scholar
[7]	Girshick R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, 2015: 1440–1448. https://doi.org/10.1109/ICCV.2015.169. Google Scholar
[8]	He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 2980–2988. https://doi.org/10.1109/ICCV.2017.322. Google Scholar
[9]	Sun X D, Wu P C, Hoi S C H. Face detection using deep learning: an improved faster RCNN approach[J]. Neurocomputing, 2018, 299: 42−50. doi: 10.1016/j.neucom.2018.03.030 CrossRef Google Scholar
[10]	穆春阳, 李闯, 马行, 等. 改进YOLOv7-tiny的轻量化大型铸件焊缝缺陷检测[J]. 组合机床与自动化加工技术, 2024, (7): 156−160. doi: 10.13462/j.cnki.mmtamt.2024.07.032 CrossRef Google Scholar Mu C Y, Li C, Ma X, et al. Improved YOLOv7-tiny lightweight large casting weld defect detection[J]. Modular Mach Tool Autom Manuf Tech, 2024, (7): 156−160. doi: 10.13462/j.cnki.mmtamt.2024.07.032 CrossRef Google Scholar
[11]	李其鹏, 缪海波, 李志文, 等. 基于改进YOLOv7-tiny焊缝表面缺陷检测算法[J]. 焊接技术, 2024, 53(7): 123−126. doi: 10.13846/j.cnki.cn12-1070/tg.2024.07.012 CrossRef Google Scholar Li Q P, Miao H B, Li Z W, et al. Weld surface defect detection based on the improved YOLOv7-tiny algorithm[J]. Weld Technol, 2024, 53(7): 123−126. doi: 10.13846/j.cnki.cn12-1070/tg.2024.07.012 CrossRef Google Scholar
[12]	宋杰三. 基于改进Yolov8的焊缝缺陷检测研究[J]. 中国设备工程, 2024, (14): 196−199 Google Scholar Song J S. Research on weld defect detection based on improved Yolov8[J]. China Plant Engineering, 2024, (14): 196−199 Google Scholar
[13]	张恩召, 李磊, 汪建华, 等. 基于改进YOLOv8的船板焊缝质量检测方法[J]. 陕西科技大学学报, 2024, 42(6): 172−179. doi: 10.3969/j.issn.1000-5811.2024.06.021 CrossRef Google Scholar Zhang E Z, Li L, Wang J H, et al. Improved YOLOv8-based quality inspection method for ship plate welds[J]. J Shaanxi Univ Sci Technol, 2024, 42(6): 172−179. doi: 10.3969/j.issn.1000-5811.2024.06.021 CrossRef Google Scholar
[14]	李先旺, 贺岁球, 贺德强, 等. 基于改进YOLOv8的地铁列车焊缝缺陷轻量化检测方法[J]. 广西大学学报(自然科学版), 2024, 49(3): 540−552. doi: 10.13624/j.cnki.issn.1001-7445.2024.0540 CrossRef Google Scholar Li X W, He S Q, He D Q, et al. Lightweight detection method for weld defects of metro train based on improved YOLOv8[J]. J Guangxi Univ (Nat Sci Ed), 2024, 49(3): 540−552. doi: 10.13624/j.cnki.issn.1001-7445.2024.0540 CrossRef Google Scholar
[15]	吴磊, 储钰昆, 杨洪刚, 等. 面向铝合金焊缝DR图像缺陷的Sim-YOLOv8目标检测模型[J]. 中国激光, 2024, 51(16): 1602103. doi: 10.3788/CJL231485 CrossRef Google Scholar Wu L, Chu Y K, Yang H C, et al. Sim-YOLOv8 object detection model for DR image defects in aluminum alloy welds[J]. Chin J Lasers, 2024, 51(16): 1602103. doi: 10.3788/CJL231485 CrossRef Google Scholar
[16]	Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017: 936–944. https://doi.org/10.1109/CVPR.2017.106. Google Scholar
[17]	Wei H R, Liu X, Xu S C, et al. DWRSeg: rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation[Z]. arXiv: 2212.01173, 2022. https://arxiv.org/abs/2212.01173. Google Scholar
[18]	Wang J, Chen K, Xu R, et al. CARAFE: content-aware ReAssembly of features[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, 2019: 3007–3016. https://doi.org/10.1109/ICCV.2019.00310. Google Scholar
[19]	Li H L, Li J, Wei H B, et al. Slim-neck by GSConv: a lightweight-design for real-time detector architectures[J]. J Real Time Image Process, 2022, 21(3): 62. doi: 10.1007/s11554-024-01436-6 CrossRef Google Scholar
[20]	Zhang H, Xu C, Zhang S J. Inner-IoU: more effective intersection over union loss with auxiliary bounding box[Z]. arXiv: 2311.02877, 2023. https://arxiv.org/abs/2311.02877. Google Scholar
[21]	Gevorgyan Z. SIoU loss: more powerful learning for bounding box regression[Z]. arXiv: 2205.12740, 2022. https://arxiv.org/abs/2205.12740. Google Scholar
[22]	Rezatofighi H, Tsoi N, Gwak J, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 658–666. https://doi.org/10.1109/CVPR.2019.00075. Google Scholar
[23]	Zhang Y F, Ren W Q, Zhang Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146−157. doi: 10.1016/j.neucom.2022.07.042 CrossRef Google Scholar
[24]	Zheng Z H, Wang P, Liu W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020: 12993–13000. https://doi.org/10.1609/aaai.v34i07.6999. Google Scholar
[25]	Tong Z J, Chen Y H, Xu Z W, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[Z]. arXiv: 2301.10051, 2023. https://arxiv.org/abs/2301.10051. Google Scholar
[26]	Huang Y, Tan W, Li L, et al. WFRE-YOLOv8s: a new type of defect detector for steel surfaces[J]. Coatings, 2023, 13(12): 2010. doi: 10.3390/coatings13122010 CrossRef Google Scholar

Overview

Overview

The welding quality of steel thin plates is of paramount importance in modern industrial production, with its widespread use in sectors such as intelligent manufacturing, industrial construction, and aerospace. Weld defects can lead to significant safety and performance issues, making the detection of these flaws a critical task. In light of this, this paper introduces an enhanced YOLOv8-based algorithm for weld surface defect detection, named GD-YOLO. The development of GD-YOLO begins with the introduction of a novel feature extraction module, C2f-DWR, which integrates the lightweight attention mechanism DWR with the feature extraction module C2f. This replacement of the original C2f module is designed to boost the model's efficiency in gathering information during real-time detection. The integration of the Slim-Neck structure into the neck network further reduces the algorithm's complexity and enhances its ability to detect the rough edges of defects. The algorithm also incorporates the upsampling operator CAFARE in the feature fusion stage, replacing the traditional Upsample operator. This change is aimed at increasing the resolution of feature maps and the transmission of semantic information, which is vital for accurate defect detection. Additionally, the improved attention mechanism module GD-CBAM is incorporated into the backbone network of YOLOv8, which not only accelerates the inference speed but also ensures that the model remains lightweight and efficient. To address the common issue of mismatch between the true and predicted bounding boxes, the GD-YOLO model employs the Inner-SIOU bounding box regression loss function. This function is specifically designed to minimize the discrepancies between the actual defect locations and the model's predictions. Empirical evidence from experiments demonstrates that the proposed GD-YOLO model outperforms the original YOLOv8 by 7.8% in the mAP0.5 detection metric, a significant improvement in accuracy. Moreover, the model shows a reduction of 0.2 M in parameter quantity and 0.7 G in computational load, making it more efficient than its predecessor. Compared to other target defect detection models, GD-YOLO exhibits a clear advantage in terms of detection accuracy. Ablation experiments conducted to validate the effectiveness of each module within the GD-YOLO framework confirm that each component contributes positively to the overall performance of the model. Furthermore, generalization experiments substantiate the improved algorithm's ability to perform well across different datasets, indicating its robustness and versatility in various real-world applications. In conclusion, GD-YOLO represents a significant advancement in steel thin plate weld defect detection, offering a more accurate, efficient, and reliable solution for industrial quality control.