We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Towards More Efficient EfficientDets and Real-Time Marine Debris Detection
Summary
Researchers improved the efficiency of a class of AI-based object detection systems called EfficientDets for real-time identification of marine debris underwater. Their optimized models achieved better accuracy while running faster, making them more practical for use on autonomous underwater vehicles. This technology could help enable automated detection and removal of ocean plastic waste, which breaks down into harmful microplastics over time.
Marine debris is a problem both for the health of marine environments and for the human health since tiny pieces of plastic called “microplastics” resulting from the debris decomposition over the time are entering the food chain at any levels. For marine debris detection and removal, autonomous underwater vehicles (AUVs) are a potential solution. In this letter, we focus on the efficiency of AUV vision for real-time marine debris detection. First, we improved the efficiency of a class of state-of-the-art object detectors, namely EfficientDets [1], by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3 without increasing the GPU latency (see Fig. 1 ). Subsequently, we created and made publicly available a dataset for the detection of in-water plastic bags and bottles and trained our improved EfficientDets on this and on two public datasets for marine debris detection. Finally, we began the testing of real-time detection performance on a simulator of marine environments.
Sign in to start a discussion.
More Papers Like This
Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection
Researchers improved the computational efficiency of EfficientDet object detectors and applied them to real-time marine debris detection for autonomous underwater vehicles, achieving AP gains of 1.2-2.6% without increased GPU latency, creating a new in-water plastic bag and bottle dataset, and investigating detection performance degradation under low-light underwater conditions.
Efficient Object Detection of Marine Debris using Pruned YOLO Model
Researchers applied a pruned YOLOv4 object detection model to identify marine debris, including microplastic-contaminated items, in underwater images. Channel pruning reduced the model size by 90% while maintaining detection accuracy, making it suitable for deployment on autonomous underwater vehicles. The study demonstrates that efficient machine learning models can support real-time marine debris detection and cleanup efforts.
YOLOv8-C2f-Faster-EMA: An Improved Underwater Trash Detection Model Based on YOLOv8
Researchers improved an AI-based object detection system (YOLOv8) to better identify small pieces of underwater trash, achieving a 5% improvement in detection accuracy. Automated trash detection in waterways matters because removing plastic waste before it breaks into microplastics can reduce the amount of tiny plastic particles that eventually contaminate drinking water and seafood.
AI – Driven Marine Debris Detection for Ocean Conservation
Researchers developed an AI-driven marine debris detection system using the YOLOv8 deep learning model trained to identify plastic waste in challenging underwater conditions including low visibility and complex backgrounds. The system aims to provide scalable, automated monitoring to support ocean conservation and guide debris removal efforts.
PBM‐YOLO: A Performance Balanced Floating Garbage Detection Model for Water Surface Environments
Researchers developed PBM-YOLO, a performance-balanced deep learning model for detecting floating garbage including plastic debris on water surfaces, optimising the architecture to balance detection accuracy and computational efficiency for practical deployment in ecological protection and waterway resource recycling applications.