{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:29:51Z","timestamp":1778495391682,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"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>Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm\u2019s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.<\/jats:p>","DOI":"10.3390\/s21217269","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Coastal Waste Detection Based on Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9958-0476","authenticated-orcid":false,"given":"Chengjuan","family":"Ren","sequence":"first","affiliation":[{"name":"Software Convergence Engineering Department, Kunsan National University, Gunsan 54150, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyunjun","family":"Jung","sequence":"additional","affiliation":[{"name":"Software Convergence Engineering Department, Kunsan National University, Gunsan 54150, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3390-5602","authenticated-orcid":false,"given":"Sukhoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Software Convergence Engineering Department, Kunsan National University, Gunsan 54150, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9881-5336","authenticated-orcid":false,"given":"Dongwon","family":"Jeong","sequence":"additional","affiliation":[{"name":"Software Convergence Engineering Department, Kunsan National University, Gunsan 54150, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"191","DOI":"10.12911\/22998993\/78747","article-title":"Assessment of Comprehensive Environmental Pollution Index of Kurichi Industrial Cluster, Coimbatore District, Tamil Nadu, India\u2014A Case Study","volume":"19","author":"Ramasamy","year":"2018","journal-title":"J. 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