{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:08:23Z","timestamp":1775066903340,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Multimedia University, Cyberjaya","award":["MMUI\/240029"],"award-info":[{"award-number":["MMUI\/240029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>Increased waste volume and limitations of traditional separation methods have made waste management a hot topic in recent years. To enable the recycling process to be optimized and to minimize environmental impact, waste materials must be well detected and classified. Building on this research, the system is an automated waste-detecting system that integrates machine vision and artificial intelligence (AI). It is coupled with advanced convolutional neural networks (CNNs), which are used for data collection, real-time waste detection, and classification of the proposed framework. Images of waste were captured in many different settings and analyzed with a YOLOv12-based model. The system achieves more gain in detecting and categorizing waste types with 73% precision and a mean average precision (mAP) of 78% in 100 epochs. Results indicate that the YOLOv12 model surpasses the current detection algorithms to provide an efficient and scalable solution to waste management challenges.<\/jats:p>","DOI":"10.3390\/digital5020019","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T06:46:01Z","timestamp":1749451561000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1036-033X","authenticated-orcid":false,"given":"Mosharof Hossain","family":"Dipo","sequence":"first","affiliation":[{"name":"Department of Computer Science, American International University\u2014Bangladesh, Dhaka 1229, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2625-2348","authenticated-orcid":false,"given":"Fahmid Al","family":"Farid","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}]},{"given":"Md. Sifti Al","family":"Mahmud","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American International University\u2014Bangladesh, Dhaka 1229, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0986-483X","authenticated-orcid":false,"given":"Muntasir","family":"Momtaz","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American International University\u2014Bangladesh, Dhaka 1229, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6375-4174","authenticated-orcid":false,"given":"Shakila","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American International University\u2014Bangladesh, Dhaka 1229, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3403-4095","authenticated-orcid":false,"given":"Jia","family":"Uddin","sequence":"additional","affiliation":[{"name":"AI and Big Data Department, Woosong University, Daejeon 34606, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7613-4596","authenticated-orcid":false,"given":"Hezerul Abdul","family":"Karim","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100891","DOI":"10.1016\/j.rineng.2023.100891","article-title":"Intelligent detection and waste control of hawthorn fruit based on ripening level using machine vision system and deep learning techniques","volume":"17","author":"Azadnia","year":"2023","journal-title":"Results Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.wasman.2021.12.001","article-title":"Deep learning-based waste detection in natural and urban environments","volume":"138","author":"Majchrowska","year":"2022","journal-title":"Waste Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2072","DOI":"10.1016\/j.jksuci.2020.08.016","article-title":"Intelligent waste management system using deep learning with IoT","volume":"34","author":"Rahman","year":"2022","journal-title":"J. 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