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The accurate and timely detection of such waste is essential for enabling autonomous cleanup sys-tems like unmanned surface vehicles (USVs). However, detecting floating waste remains challenging due to the small size of debris, water surface reflections, glare, and complex backgrounds. This study presents a comparative evaluation of state-of-the-art deep learning-based object detection models\u2014YOLO (v8\u2013v10), Faster R-CNN, and Real-Time Detection Transformer (RT-DETR)\u2014using the FloW-Img dataset, which is specifically designed for floating waste detection from USV perspectives. To enhance detection performance, we also explored four ensemble strategies: Weighted Box Fusion (WBF), Non-Maximum Suppression (NMS), Soft-NMS, and Non-Maximum Weighted (NMW). Our experiments show that the ensemble of RT-DETR-X and Faster R-CNN using WBF achieves the best results, with a mean Average Precision (mAP50) of 89.081%. This performance surpasses all previously reported methods on the same dataset, including YOLO-Float and Cascade R-CNN. The findings demonstrate the effectiveness of deep learning ensembles in improving small object detection in challenging water environments. This comparative study contributes valuable insights for developing robust, real-time, and scalable solutions for environmental monitoring and automated waste management systems.<\/jats:p>","DOI":"10.1007\/s00521-026-12051-w","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T03:45:53Z","timestamp":1776051953000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Floating waste detection using deep learning: a comparative study of YOLO, RT-DETR, and faster R-CNN"],"prefix":"10.1007","volume":"38","author":[{"given":"Shaheenur Islam","family":"Sumon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. 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The study does not involve any human participants or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"293"}}