ArticleScientific reports2024
Water surface garbage detection based on lightweight YOLOv5.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ESAK-YOLO: multi-scale attention enhanced object detection for floating waste in complex water scenes.Scientific reports · 2026Article
- Research on the intelligent detection and analysis of floating debris in polluted water.Scientific reports · 2026Article
- Small target detection of floating objects in river channels based on improved YOLOv7.Scientific reports · 2026Article
- Improved YOLOv8-SST for accurate detection of small floating objects in complex water environments.PloS one · 2026Article
- An enhanced YOLOv8 model for accurate detection of solid floating waste.Scientific reports · 2025Article
- A New Algorithm for Visual Navigation in Unmanned Aerial Vehicle Water Surface Inspection.Sensors (Basel, Switzerland) · 2025Article
- An MRS-YOLO Model for High-Precision Waste Detection and Classification.Sensors (Basel, Switzerland) · 2024Article
- Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms.Sensors (Basel, Switzerland) · 2024Article
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of deep learning technology, researchers are increasingly paying attention to how to efficiently salvage surface garbage. Since the 1980s, the development of plastic products and economic growth has led to the accumulation of a large amount of garbage in rivers. Due to the large amount of garbage and the high risk of surface operations, the efficiency of manual garbage retrieval will be greatly reduced. Among existing methods, using YOLO algorithm to detect target objects is the most popular. Compared to traditional detection algorithms, YOLO algorithm not only has higher accuracy, but also is more lightweight. This article presents a lightweight YOLOv5 water surface garbage detection algorithm suitable for deployment on unmanned ships. This article has been validated on the Orca dataset, experimental results showed that the detection speed of the improved YOLOv5 increased by 4.3%, mAP value reached 84.9%, precision reached 88.7%, the parameter quantity only accounts for 12% of the original data. Compared with the original algorithm, the improved algorithm not only has higher accuracy, but also can be applied to more hardware devices due to its lighter weight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.