{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T19:39:29Z","timestamp":1782761969693,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:00:00Z","timestamp":1573862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB1308000"],"award-info":[{"award-number":["2018YFB1308000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/s19225000","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T04:31:10Z","timestamp":1574051470000},"page":"5000","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN"],"prefix":"10.3390","volume":"19","author":[{"given":"Zhuangzhuang","family":"Zhou","sequence":"first","affiliation":[{"name":"Automation College, Foshan University, Foshan 528000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Foshan University, Foshan 528000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Foshan University, Foshan 528000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojie","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Foshan University, Foshan 528000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9641","DOI":"10.1109\/TIE.2019.2896165","article-title":"A smart monitoring system for automatic welding defect detection","volume":"66","author":"Sassi","year":"2019","journal-title":"IEEE Trans. Industrial Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4197","DOI":"10.1007\/s00500-017-2709-1","article-title":"Automatic image thresholding using Otsu\u2019s method and entropy weighting scheme for surface defect detection","volume":"13","author":"Truong","year":"2018","journal-title":"Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s40684-016-0039-x","article-title":"Machine learning-based imaging system for surface defect inspection","volume":"3","author":"Park","year":"2016","journal-title":"Int. J. Precis. Eng. Manuf.-Green Technol."},{"key":"ref_4","first-page":"1","article-title":"Review of vision-based steel surface inspection systems","volume":"50","author":"Neogi","year":"2014","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.1016\/j.ijleo.2013.11.070","article-title":"Automatic defect recognition of TFT array process using gray level cooccurrence matri","volume":"125","author":"Yang","year":"2014","journal-title":"Opt.-Int. J. Light Electron Opt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.measurement.2014.10.009","article-title":"Detection and classification of surface defects of gun barrels using computer vision and machine learning","volume":"60","author":"Shanmugamani","year":"2015","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.patcog.2016.11.021","article-title":"Automatic detection and classification of the ceramic tiles\u2019 surface defects","volume":"66","author":"Saeed","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1109\/TIM.2012.2184959","article-title":"A real-time visual detection system for discrete surface defects of rail heads","volume":"61","author":"Li","year":"2012","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1109\/TIM.2013.2283741","article-title":"Automatic fastener classification and defect detection invision-based railway detection systems","volume":"63","author":"Feng","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks, Curran Associates Inc."},{"key":"ref_11","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_12","first-page":"1117209","article-title":"Deflectometric data segmentation based on fully convolutional neural networks","volume":"Volume 11172","author":"Balzategu","year":"2019","journal-title":"Fourteenth International Conference on Quality Control by Artificial Vision"},{"key":"ref_13","first-page":"477","article-title":"A steel defect image segmentation method based on target0s area characteristic analysis","volume":"29","author":"Zhao","year":"2015","journal-title":"Xi\u2019an Polytech. Univ."},{"key":"ref_14","first-page":"1","article-title":"Railway subgrade defect automatic recognition method based on improved Faster R-CNN","volume":"2018","author":"Xu","year":"2018","journal-title":"Sci. Program."},{"key":"ref_15","first-page":"336","article-title":"Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining","volume":"2","author":"Tianyu","year":"2017","journal-title":"Sensors"},{"key":"ref_16","first-page":"1","article-title":"Fast Multiclass Vehicle Detection on Aerial Images","volume":"12","author":"Liu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kluckner, S., Pacher, G., Grabner, H., and Bischof, H. (2007, January 14\u201321). A 3D Teacher for Car Detection in Aerial Images. Proceedings of the IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4408834"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TGRS.2013.2253108","article-title":"Automatic Car Counting Method for Unmanned Aerial Vehicle Images","volume":"52","author":"Moranduzzo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-Based Convolutional Networks for Accurate Object Detection and Segmentation","volume":"38","author":"Girshick","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective search for object recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_22","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2013). Visualizing and Understanding Convolutional Networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_25","first-page":"2615","article-title":"A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes","volume":"63","author":"Peng","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ghodrati, A., Diba, A., Pedersoli, M., Tuytelaars, T., and Gool, L.V. (2015, January 7\u201313). DeepProposal: Hunting Objects by CascadingDeep Convolutional Layers. Proceedings of the IEEE International Conference on Computer Vision, Los Alamitos, CA, USA.","DOI":"10.1109\/ICCV.2015.296"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wen, S., Chen, Z., and Li, C. (2018). Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks. Appl. Sci., 8.","DOI":"10.3390\/app8122565"},{"key":"ref_28","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/5000\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:35:03Z","timestamp":1760189703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/5000"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,16]]},"references-count":29,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19225000"],"URL":"https:\/\/doi.org\/10.3390\/s19225000","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,16]]}}}