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Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection
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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.
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 and low-light object detection. First, we improved the efficiency of a class of state-of-the-art object detectors, namely EfficientDets, 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. 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 another dataset for marine debris detection. Finally, we investigated how the detector performance is affected by low-light conditions and compared two low-light underwater image enhancement strategies both in terms of accuracy and latency. Source code and dataset are publicly available.
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Towards More Efficient EfficientDets and Real-Time Marine Debris Detection
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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.
Efficient Object Detection of Marine Debris using Pruned YOLO Model
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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.
Deep Learning-Based Underwater Trash Detection System UsingYOLOv8
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Researchers trained a YOLOv8 deep learning model on underwater debris imagery to enable real-time detection and classification of submerged plastic trash, demonstrating substantially faster and more scalable performance than traditional manual survey or sonar-based methods.
LE-RTDETR: multi-scale edge enhancement and structural compression for underwater debris detection
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Researchers built a faster, lighter AI tool that spots trash in murky underwater photos more accurately than previous models, even with blurry visibility and distorted colors. This matters because better detection of ocean debris, including plastic waste that breaks down into microplastics, could help cleanup efforts target pollution that eventually affects marine life and the seafood we eat.
Deep learning-based detection of submerged debris and plastics in underwater environments: a systematic review
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This review pulls together 50 studies on AI systems that use underwater cameras to spot plastic debris in oceans and waterways, finding that certain smart, efficient algorithms can now detect trash with over 90% accuracy—even running on small, low-power devices carried by underwater robots. This matters because better tracking of where plastic waste accumulates and breaks down is a key step toward understanding and eventually reducing the microplastics that end up in seafood, drinking water, and our bodies, though the technology still struggles in murky or dark water.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.