Research on key technologies and applications of AI-enabled industrial product defect detection

doi:10.12267/j.issn.2096-5931.2025.08.010

Abstract

Abstract:

With the rapid development of artificial intelligence (AI) technology, defect inspection for industrial products is evolving from traditional manual visual inspection and image processing methods towards intelligent and generalized approaches. This paper systematically reviews the types of industrial product defect detection and the path of technological development, makes an in-depth analysis of the technological evolution from traditional image processing to deep learning and then to the basic model stage, focuses on the current key technological breakthroughs in the areas of data augmentation, few-shot learning, model lightweight and cloud-edge-end collaboration. Through practical application cases in industries including consumer electronics, steel, new energy batteries, tires, and textiles, the paper demonstrates the significant effects of efficiency improvement, cost reduction and quality assurance brought by AI-enabled defect detection, providing technical support and practical reference for the intelligent transformation of the manufacturing industry.

Key words: defect inspection, foundation models, data augmentation

CLC Number:

TP18
TN929.5

DING Yixin, YU Wenming, DONG Hao. Research on key technologies and applications of AI-enabled industrial product defect detection[J]. Information and Communications Technology and Policy, 2025, 51(8): 71-77.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2025.08.010

http://ictp.caict.ac.cn/EN/Y2025/V51/I8/71

References 29

[1]	JAIN S, SETH G, PARUTHI A, et al. Synthetic data augmentation for surface defect detection and classification using deep learning[J]. Journal of Intelligent Manufacturing, 2020, 33(4):1007-1020.
[2]	HE X, CHANG Z, ZHANG L, et al. A survey of defect detection applications based on generative adversarial networks[J]. IEEE Access, 2022,10:113493-113512.
[3]	GAO Y, LI X, WANG V, et al. A review on recent advances in vision-based defect recognition towards industrial intelligence[J]. Journal of Manufacturing Systems, 2022,62:753-766.
[4]	RASOUL A, CHUNG-CHIAN H, SHAHAB B. A systematic review of deep learning approaches for surface defect detection in industrial applications[J]. Engineering Applications of Artificial Intelligence, 2024,130:107717.
[5]	RIQUELME C, PUIGCERVER J, MUSTAFA B, et al. Scaling vision with sparse mixture of experts[J]. arXiv Preprint, arXiv:2106.05974, 2021.
[6]	SHAO J, CHEN S Y, LI Y G, et al. INTERN: a new learning paradigm towards general vision[J]. arXiv Preprint, arXiv:2111.08687, 2022.
[7]	DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[J]. arXiv Preprint, arXiv:2010.11929, 2020.
[8]	BAO H B, DONG L, PIAO S H, et al. BEiT: BERT pre-training of image transformers[J]. arXiv Preprint, arXiv:2106.08254, 2022.
[9]	张燚钧, 张润清, 周华健, 等. 视觉基础模型研究现状与发展趋势[J]. 中国图象图形学报, 2025, 30(1):1-24.
[10]	GU Z, ZHU B, ZHU G, et al. AnomalyGPT: detecting industrial anomalies using large vision-language models[J]. arXiv Preprint, arXiv:2308.15366, 2023.
[11]	ZUO Z, DONG J, WU Y, et al. Clip3d-ad: extending clip for 3d few-shot anomaly detection with multi-view images generation[J]. arXiv Preprint, arXiv:2406.18941, 2024.
[12]	YANG T, CHANG L, YAN J, et al. A survey on foundation-model-based industrial defect detection[J]. arXiv Preprint, arXiv:2502.19106, 2025.
[13]	ZOU Y, JEONG J, PEMULA L, et al. Spot-the-difference self-supervised pre-training for anomaly detection and segmentation[C]// European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022:392-408.
[14]	WANG C, ZHU W, GAO B, et al. Real-iad: a real-world multi-view dataset for benchmarking versatile industrial anomaly detection[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024: 22883-22892.
[15]	CAPOGROSSO L, GIRELLA F, TAIOLI F, et al. Diffusion-based image generation for in-distribution data augmentation in surface defect detection[J]. arXiv Preprint, arXiv:2406.00501, 2024.
[16]	TAI Y, YANG K, PENG T, et al. Defect image sample generation with diffusion prior for steel surface defect recognition[J]. arXiv Preprint, arXiv:2405.01872, 2024.
[17]	SNEHA K, SHENBAGA S V, SIVAMANI B, et al. Fabric defect detection using transfer learning[J]. International Journal of Research In Science & Amp Engineering, 2024: 62-71.
[18]	PANG S, ZHAO W, WANG S, et al. Permute-MAML: exploring industrial surface defect detection algorithms for few-shot learning[J]. Complex & Intelligent Systems, 2024, 10(1):1473-1482.
[19]	ZHANG Z, DONG C, WEI Z, et al. GCB-YOLO: a lightweight algorithm for wind turbine blade defect detection[J]. Wind Energy, 2025, 28(6):e70029.
[20]	LI J, CHENG M. FBS-YOLO: an improved lightweight bearing defect detection algorithm based on YOLOv8[J]. Physica Scripta, 2025,2:100.
[21]	LEI Z, ZHANG Y, WANG J, et al. Cloud-edge collaborative defect detection based on efficient yolo networks and incremental learning[J]. Sensors, 2024, 24(18):5921.
[22]	LIANG D, ZHANG H, HAN Q, et al. RasPiDets: a quasi-real-time defect detection method with end-edge-cloud collaboration[J]. IEEE Transactions on Industrial Informatics, 2025, 21(7):5525-5535.
[23]	工业互联网产业联盟, 中国信息通信研究院. 工业大模型技术应用与发展报告1.0[EB/OL]. (2023-12-26)[2025-06-28]. https://www.aii-alliance.org/resource/c331/n4797.html.
[24]	程荫. 中国AI赋能的工业质检解决方案市场分析[J]. 电子产品世界, 2021, 28(9):7-9,66.
[25]	人工智能创新应用洞察. 兴智新观察\|AI起航秀-联想乐眼工业质检方案[EB/OL]. (2023-08-31)[2025-06-28]. https://mp.weixin.qq.com/s/FkEeCklOGOadGkEOyYirFA.
[26]	中国钢铁工业协会. 宝钢股份AI转型的深度实践[EB/OL]. (2025-06-19)[2025-06-28]. https://www.chinaisa.org.cn/gxportal/xfgl/portal/content.html?articleId=e4ce7b24517ca66973dac996f437c7c47460e4c30d7990c2bc75ca88743284c6&columnId=268f86fdf61ac8614f09db38a2d0295253043b03e092c7ff48ab94290296125c.
[27]	机器视觉产业联盟. 工业质检母机软件:联想边缘大脑V3.0[EB/OL]. (2025-04-22)[2025-06-29]. https://mp.weixin.qq.com/s/3wU1Hx0fiEDgtzgQ7uUSYA.
[28]	联想研究院. 国内首台轮胎外观缺陷全检AI设备[EB/OL]. (2025-05-23)[2025-06-29]. https://mp.weixin.qq.com/s/BmsDqS0OZHaCHjRKUhV-zA.
[29]	联想研究院. 这台AI设备, 让纺织业质检效率飙升300%![EB/OL]. (2025-06-27)[2025-06-29]. https://mp.weixin.qq.com/s/8KbiIXsqJpA9HL0d2jq6fQ.