{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:35:16Z","timestamp":1771515316129,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016078","name":"Fuzhou Science and Technology Bureau","doi-asserted-by":"publisher","award":["FZKJ202409ZB04"],"award-info":[{"award-number":["FZKJ202409ZB04"]}],"id":[{"id":"10.13039\/100016078","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the rise of smart manufacturing, defect detection in small-size liquid crystal display (LCD) screens has become essential for ensuring product quality. Traditional manual inspection is inefficient and labor-intensive, making it unsuitable for modern automated production. Although machine vision techniques offer improved efficiency, the lack of high-quality defect datasets limits their performance. To overcome this, we propose a symmetry-aware generative framework, the Squeeze-and-Excitation Wasserstein GAN with Gradient Penalty and Visual Geometry Group(VGG)-based perceptual loss (SWG-VGG), for realistic defect image synthesis.By leveraging the symmetry of feature channels through attention mechanisms and perceptual consistency, the model generates high-fidelity defect images that align with real-world structural patterns. Evaluation using the You Only Look Once version 8(YOLOv8) detection model shows that the synthetic dataset improves mAP@0.5 to 0.976\u2014an increase of 10.5% over real-data-only training. Further assessment using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Root Mean Square Error (RMSE), and Content Similarity (CS) confirms the visual and structural quality of the generated images.This symmetry-guided method provides an effective solution for defect data augmentation and aligns closely with Symmetry\u2019s emphasis on structured pattern generation in intelligent vision systems.<\/jats:p>","DOI":"10.3390\/sym17060833","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T04:46:38Z","timestamp":1748493998000},"page":"833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An LCD Defect Image Generation Model Integrating Attention Mechanism and Perceptual Loss"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9050-1137","authenticated-orcid":false,"given":"Sheng","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Nancheng County Institute of Machine Vision Industry Technology, Fuzhou 344700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6372-7044","authenticated-orcid":false,"given":"Yuxin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Nancheng County Institute of Machine Vision Industry Technology, Fuzhou 344700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8035-7257","authenticated-orcid":false,"given":"Xiaoyue","family":"Chen","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan 430205, China"},{"name":"School of Information Engineering, Hubei University of Economics, Wuhan 430205, China"},{"name":"Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan 430205, China"},{"name":"Nancheng County Institute of Machine Vision Industry Technology, Fuzhou 344700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2152-9200","authenticated-orcid":false,"given":"Shi","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Nancheng County Institute of Machine Vision Industry Technology, Fuzhou 344700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.rcim.2022.102470","article-title":"Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing","volume":"80","author":"Li","year":"2023","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s10845-023-02110-7","article-title":"Real-time defect detection of TFT-LCD displays using a lightweight network architecture","volume":"35","author":"Chen","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2023.01.018","article-title":"A method of citrus epidermis defects detection based on an improved YOLOv5","volume":"227","author":"Hu","year":"2023","journal-title":"Biosyst. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.11834\/jig.230518","article-title":"A review of vision-based defect detection methods for LCD\/OLED screens","volume":"29","author":"Lin","year":"2024","journal-title":"J. Image Graph."},{"key":"ref_7","first-page":"198","article-title":"Research progress of surface defect detection methods based on machine vision","volume":"43","author":"Zhao","year":"2022","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1007\/s10462-021-10066-4","article-title":"A comprehensive survey of recent trends in deep learning for digital images augmentation","volume":"55","author":"Khalifa","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1007\/s10115-023-01853-2","article-title":"A survey of automated data augmentation algorithms for deep learning-based image classification tasks","volume":"65","author":"Yang","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_12","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_13","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00414-0","article-title":"A survey on generative adversarial networks for imbalance problems in computer vision tasks","volume":"8","author":"Sampath","year":"2021","journal-title":"J. Big Data"},{"key":"ref_15","first-page":"1611","article-title":"Defect image sample generation with GAN for improving defect recognition","volume":"17","author":"Niu","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1177\/01423312221140940","article-title":"Generative adversarial network\u2013assisted image classification for imbalanced tire X-ray defect detection","volume":"45","author":"Gao","year":"2023","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_17","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_18","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_19","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_20","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017, January 4\u20139). Improved training of wasserstein gans. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Li, F.-F. (2016, January 11\u201314). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Part II 14.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Snell, J., Ridgeway, K., Liao, R., Roads, B.D., Mozer, M.C., and Zemel, R.S. (2017, January 17\u201320). Learning to generate images with perceptual similarity metrics. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297089"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/833\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:41:17Z","timestamp":1760031677000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/833"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":24,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["sym17060833"],"URL":"https:\/\/doi.org\/10.3390\/sym17060833","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,27]]}}}