{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:34:04Z","timestamp":1760402044871,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"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>To achieve an automatic unloading of a reactor during the sherardizing process, it is necessary to calculate the pose and position of the reactors in an industrial environment with various amounts of luminance and floating dust. In this study, the defects of classic image processing methods and deep learning methods used for locating the reactors are first analyzed. Next, an improved You Only Look Once(YOLO) model is employed to find the region of interest of the handling hole and a handling hole corner detection method based on the image morphology and a Hough transform is presented. Finally, the position and pose of the reactors will be obtained by establishing a 3D handling hole model according to the principle of a binocular stereo system. To test the performance of the proposed method, a set of experimental systems was set up and experiments were conducted. The results indicate that the proposed location method is effective and the precision of the position recognition can be controlled to within 4.64 mm and     1.68 \u00b0     when the cameras are approximately 5 m away from the reactor, meeting the requirements.<\/jats:p>","DOI":"10.3390\/s20020504","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T04:14:41Z","timestamp":1579234481000},"page":"504","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Visual Locating of Reactor in an Industrial Environment Using the Composite Method"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-2555","authenticated-orcid":false,"given":"Chenguang","family":"Cao","sequence":"first","affiliation":[{"name":"School of Automation, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Ouyang","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiamu","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.surfcoat.2014.12.051","article-title":"Impact of zinc halide addition on the growth of zinc-rich layers generated by sherardizing","volume":"263","author":"Wortelen","year":"2015","journal-title":"Surf. Coat. Technol."},{"key":"ref_2","unstructured":"Burri, M., Oleynikova, H., Achtelik, M.W., and Siegwart, R. (October, January 28). Real-time visual-inertial mapping, re-localization and planning onboard MAVs in unknown environments. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.sna.2004.05.019","article-title":"A global calibration method for large-scale multi-sensor visual measurement systems","volume":"116","author":"Lu","year":"2004","journal-title":"Sens. Actuators A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37965","DOI":"10.1109\/ACCESS.2018.2852663","article-title":"Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation","volume":"6","author":"Huangpeng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6822","DOI":"10.1364\/AO.56.006822","article-title":"3d pose estimation of large and complicated workpieces based on binocular stereo vision","volume":"56","author":"Luo","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7261","DOI":"10.1109\/TIE.2017.2694399","article-title":"Automatic Welding Seam Tracking and Identification","volume":"64","author":"Li","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.compag.2014.10.016","article-title":"Detecting citrus fruits and occlusion recovery under natural illumination conditions","volume":"110","author":"Lu","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.biosystemseng.2015.12.001","article-title":"Design of an eye-in-hand sensing and servo control framework for harvesting robotics in dense vegetation","volume":"146","author":"Barth","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1142\/S0218001491000053","article-title":"Transformation-ring-projection (TRP) algorithm and its VLSI implementation","volume":"5","author":"Tang","year":"1991","journal-title":"Int. J. Pattern Recognit. Artif Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TIT.1962.1057692","article-title":"Visual pattern recognition by moment invariants","volume":"8","author":"Hu","year":"1962","journal-title":"IRE Trans. Inf. Theory"},{"key":"ref_11","unstructured":"Berthold, K.P.H. (1987). Robot Vision, The MIT Press."},{"key":"ref_12","unstructured":"Zou, Z.X., Shi, Z.W., Guo, Y.H., and Ye, J.P. (2019). Object Detection in 20 Years: A Survey. arxiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"68281","DOI":"10.1109\/ACCESS.2019.2916842","article-title":"Broken Corn Detection Based on an Adjusted YOLO With Focal Loss","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_18","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_20","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arxiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, C.S., Guo, Y., Li, S., and Chang, F.L. (2019). ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution Images. Sensors, 19.","DOI":"10.3390\/s19122671"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8429","DOI":"10.1364\/AO.54.008429","article-title":"Approach for designing and developing high-precision integrative systems for strip flatness detection","volume":"54","author":"Ouyang","year":"2015","journal-title":"Appl. Opt."},{"key":"ref_24","first-page":"62","article-title":"Threshold selection method from gray-histogram","volume":"9","author":"OTSU","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/34.87344","article-title":"Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations","volume":"13","author":"Vincent","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., and Weyand, T. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/S0031-3203(00)00023-6","article-title":"On the Canny edge detector","volume":"34","author":"Ding","year":"2001","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1007\/s11227-017-2051-5","article-title":"A fast Hough Transform algorithm for straight lines detection in an image using GPU parallel computing with CUDA-C","volume":"73","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","article-title":"Apple detection during different growth stages in orchards using the improved YOLO-V3 model","volume":"157","author":"Tian","year":"2019","journal-title":"Comput. Electron. Agric."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/504\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:19:53Z","timestamp":1760361593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/504"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,16]]},"references-count":29,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20020504"],"URL":"https:\/\/doi.org\/10.3390\/s20020504","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,1,16]]}}}