{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:13:37Z","timestamp":1787242417194,"version":"build-2736575974"},"reference-count":29,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Defect detection in transparent materials typically relies on specific lighting conditions. However, through our work on defect detection for aircraft glass canopies, we found that using a single lighting condition often led to missed or false detections. This limitation arises from the optical properties of transparent materials, where certain defects only become sufficiently visible under specific lighting angles. To address this issue, we developed a dual-modal illumination system that integrates both forward and backward lighting to capture defect images. Additionally, we introduced the first dual-modal dataset for defect detection in aircraft glass canopies. Furthermore, we proposed an attention-based dual-branch modal fusion network (ADMF-Net) to enhance the detection process. Experimental results show that our system and model significantly improve the detection performance, with the dual-modal approach increasing the mAP by 5.6% over the single-modal baseline, achieving a mAP of 98.4%. Our research also provides valuable insights for defect detection in other transparent materials.<\/jats:p>","DOI":"10.3390\/s24206717","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"6717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Dual-Modal Illumination System for Defect Detection of Aircraft Glass Canopies"],"prefix":"10.3390","volume":"24","author":[{"given":"Zijian","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5051-8522","authenticated-orcid":false,"given":"Yong","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runyuan","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.cja.2019.09.017","article-title":"Successes and challenges in non-destructive testing of aircraft composite structures","volume":"33","author":"Towsyfyan","year":"2020","journal-title":"Chin. J. Aeronaut."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1080\/03602559.2015.1132452","article-title":"Energo-mechanical evaluation of damage growth and fracture initiation in aviation composite structures","volume":"55","year":"2016","journal-title":"Polym.-Plast. Technol. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104692","DOI":"10.1016\/j.engfailanal.2020.104692","article-title":"A review of failure modes and fracture analysis of aircraft composite materials","volume":"115","author":"Zimmermann","year":"2020","journal-title":"Eng. Fail. Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1108\/09576069310023838","article-title":"Manual, Hybrid and Automated Inspection Literature and Current Research","volume":"4","author":"Kopardekar","year":"1993","journal-title":"Integr. Manuf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"183192","DOI":"10.1109\/ACCESS.2020.3029127","article-title":"A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry","volume":"8","author":"Ebayyeh","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7400","DOI":"10.1109\/JSEN.2020.2978427","article-title":"Defect Detection in Aluminum Bars Using Impedance and Ultrasonic Attenuation","volume":"20","author":"Costa","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"362","DOI":"10.4028\/www.scientific.net\/AMM.437.362","article-title":"The system research on automatic defect detection of glasses","volume":"437","author":"Yao","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1109\/TII.2019.2935153","article-title":"A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform","volume":"16","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.asoc.2016.10.030","article-title":"Automatic surface defect detection for mobile phone screen glass based on machine vision","volume":"52","author":"Jian","year":"2017","journal-title":"Appl. Soft Comput. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nand, G.K., and Neogi, N. (2014, January 11\u201313). Defect detection of steel surface using entropy segmentation. Proceedings of the 11th IEEE India Conference: Emerging Trends and Innovation in Technology, INDICON 2014, Pune, India.","DOI":"10.1109\/INDICON.2014.7030439"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C.M., and Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications\u2014A SURVEY. Sensors, 20.","DOI":"10.3390\/s20051459"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.mfglet.2019.12.006","article-title":"Advanced cover glass defect detection and classification based on multi-DNN model","volume":"23","author":"Park","year":"2020","journal-title":"Manuf. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115543","DOI":"10.1016\/j.compstruct.2022.115543","article-title":"IRT-GAN: A generative adversarial network with a multi-headed fusion strategy for automated defect detection in composites using infrared thermography","volume":"290","author":"Cheng","year":"2022","journal-title":"Compos. Struct."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.patrec.2021.11.029","article-title":"HMFCA-Net: Hierarchical multi-frequency based Channel attention net for mobile phone surface defect detection","volume":"153","author":"Zhu","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"122717","DOI":"10.1016\/j.conbuildmat.2021.122717","article-title":"Image-based surface scratch detection on architectural glass panels using deep learning approach","volume":"282","author":"Pan","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hussain, M. (2023). YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. Machines, 11.","DOI":"10.3390\/machines11070677"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"80804","DOI":"10.1109\/ACCESS.2022.3195901","article-title":"Solar Cell Surface Defect Detection Based on Improved YOLO v5","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s12559-022-10061-z","article-title":"Surface Defect Detection Algorithm Based on Feature-Enhanced YOLO","volume":"15","author":"Xie","year":"2023","journal-title":"Cogn. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104042","DOI":"10.1016\/j.imavis.2020.104042","article-title":"Deep multimodal fusion for semantic image segmentation: A survey","volume":"105","author":"Zhang","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.patcog.2018.08.007","article-title":"Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection","volume":"86","author":"Chen","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Q., Chi, Y., Shen, T., Song, J., Zhang, Z., and Zhu, Y. (2022). Improving RGB-Infrared Object Detection by Reducing Cross-Modality Redundancy. Remote Sens., 14.","DOI":"10.3390\/rs14092020"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges","volume":"22","author":"Feng","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"23390","DOI":"10.1109\/JSEN.2021.3106057","article-title":"Multisensor Image Fusion for Automated Detection of Defects in Printed Circuit Boards","volume":"21","author":"Li","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","article-title":"A review: Deep learning for medical image segmentation using multi-modality fusion","volume":"3\u20134","author":"Zhou","year":"2019","journal-title":"Array"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s40684-021-00343-6","article-title":"State of the Art in Defect Detection Based on Machine Vision","volume":"9","author":"Ren","year":"2022","journal-title":"Int. J. Precis. Eng. and Manuf.-Green Tech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107722","DOI":"10.1016\/j.measurement.2020.107722","article-title":"A comprehensive review of defect detection in 3C glass components","volume":"158","author":"Ming","year":"2020","journal-title":"Measurement"},{"key":"ref_27","unstructured":"Wang, W. (2024, October 14). Advanced Auto Labeling Solution with Added Features. Available online: https:\/\/github.com\/CVHub520\/X-AnyLabeling."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109913","DOI":"10.1016\/j.patcog.2023.109913","article-title":"ICAFusion: Iterative cross-attention guided feature fusion for multispectral object detection","volume":"145","author":"Shen","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., and Huang, Z. (2023, January 4\u201310). Efficient Multi-Scale Attention Module with Cross-Spatial Learning. Proceedings of the ICASSP 2023\u20142023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10096516"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/20\/6717\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:16:31Z","timestamp":1760112991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/20\/6717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":29,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24206717"],"URL":"https:\/\/doi.org\/10.3390\/s24206717","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,18]]}}}