{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T01:01:06Z","timestamp":1792026066813,"version":"4.4.0"},"reference-count":16,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Polytechnic Institute of Mexico"},{"name":"Technological Innovation of Mexico"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We present a modular multi-attribute vehicle analysis pipeline that integrates YOLO-based models and an OCR engine into a single workflow. The system detects vehicles, classifies color, recognizes make and sub-model, detects license plates, and extracts plate characters to generate a structured vehicle record. Vehicle detection is reported with standard metrics (precision, recall, and mAP@0.5), while license plate detection is reported at IoU = 0.3 to reflect the small-object nature of plates and downstream OCR usability. Among the evaluated versions, YOLOv8 provides the most balanced overall performance across modules, while maintaining real-time-equivalent throughput of approximately 18\u201322 FPS for the full pipeline on recorded traffic videos, depending on scene complexity. We emphasize module-level evaluation and runtime benchmarking; instance-level end-to-end identification across unique vehicles is defined as future work once track-based ground truth becomes available.<\/jats:p>","DOI":"10.3390\/s26092785","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:01:18Z","timestamp":1777474878000},"page":"2785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modular Multi-Attribute Vehicle Analysis by Color, License Plate, Make and Sub-Model Using YOLO and OCR: A Benchmark Across YOLO Versions"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9956-1686","authenticated-orcid":false,"given":"Cristian Japhet","family":"Islas-Ya\u00f1ez","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n Tecnol\u00f3gica (CIITEC), Instituto Polit\u00e9cnico Nacional (IPN), Cerrada Cecati s\/n Col. Sta. Catarina, Azcapotzalco, Ciudad de M\u00e9xico 02250, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0844-1823","authenticated-orcid":false,"given":"Viridiana","family":"Hern\u00e1ndez-Herrera","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n Tecnol\u00f3gica (CIITEC), Instituto Polit\u00e9cnico Nacional (IPN), Cerrada Cecati s\/n Col. Sta. Catarina, Azcapotzalco, Ciudad de M\u00e9xico 02250, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mois\u00e9s","family":"M\u00e1rquez-Olivera","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n e Innovaci\u00f3n Tecnol\u00f3gica (CIITEC), Instituto Polit\u00e9cnico Nacional (IPN), Cerrada Cecati s\/n Col. Sta. Catarina, Azcapotzalco, Ciudad de M\u00e9xico 02250, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Artan, Y., Alkan, B., Balci, B., and Eliho\u015f, A. (2019). Deep Learning Based Vehicle Make, Model and Color Recognition Using License Plate Recognition Camera Images. Proceedings of the 2019 27th Signal Processing and Communications Applications Conference (SIU), IEEE.","DOI":"10.1109\/SIU.2019.8806465"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_3","unstructured":"(2024, October 10). Ultralytics. YOLOv8 Docs. Available online: https:\/\/docs.ultralytics.com."},{"key":"ref_4","unstructured":"(2024, October 10). Ultralytics. YOLOv9 Docs. Available online: https:\/\/docs.ultralytics.com\/models\/yolov9\/."},{"key":"ref_5","unstructured":"(2024, October 10). Ultralytics. YOLOv10 Docs. Available online: https:\/\/docs.ultralytics.com\/models\/yolov10\/."},{"key":"ref_6","unstructured":"(2024, October 10). Ultralytics. Repository. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., and Guo, X. (2022). Real-time vehicle detection based on improved YOLO v5. Sustainability, 14.","DOI":"10.3390\/su141912274"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4917097","DOI":"10.1155\/2024\/4917097","article-title":"Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems","volume":"2024","author":"Safran","year":"2024","journal-title":"J. Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ammar, A., Koubaa, A., Boulila, W., Benjdira, B., and Alhabashi, Y. (2023). A multi-stage deep-learning-based vehicle and license plate recognition system with real-time edge inference. Sensors, 23.","DOI":"10.3390\/s23042120"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Al-Hasan, T.M., Bonnefille, V., and Bensaali, F. (2024). Enhanced YOLOv8-based system for automatic number plate recognition. Technologies, 12.","DOI":"10.3390\/technologies12090164"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101885","DOI":"10.1016\/j.jksuci.2023.101885","article-title":"Two decades of vehicle make and model recognition\u2014Survey, challenges and future directions","volume":"36","author":"Gayen","year":"2024","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.patrec.2020.07.034","article-title":"Partial attention and multi-attribute learning for vehicle re-identification","volume":"138","author":"Tumrani","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_13","first-page":"250","article-title":"Integration of YOLOv8 and Paddleocr for car plate recognition system in diverse environments: Malaysia","volume":"20","author":"You","year":"2025","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Driss, M., Almomani, I., Al-Suhaimi, R., and Al-Harbi, H. (2021). Automatic Saudi Arabian license plate detection and recognition using deep convolutional neural networks. Proceedings of the International Conference of Reliable Information and Communication Technology, Springer.","DOI":"10.1007\/978-3-030-98741-1_1"},{"key":"ref_15","first-page":"835","article-title":"Multi-string missing characters restoration for automatic license plate recognition system","volume":"14","author":"Khan","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_16","unstructured":"(2024, October 25). Baidu. PaddleOCR. Available online: https:\/\/github.com\/PaddlePaddle\/PaddleOCR."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2785\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:05:17Z","timestamp":1777475117000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,29]]},"references-count":16,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26092785"],"URL":"https:\/\/doi.org\/10.3390\/s26092785","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,29]]}}}