Channel-pruned YOLOv4 raises detection speed on Trash-ICRA 19 from 15.19 to 19.4 FPS while keeping mAP near 96%, which the authors propose as an efficient model for marine debris detection on low-power hardware.
A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models
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abstract
The quantification of positively buoyant marine plastic debris is critical to understanding how plastic litter accumulates across the world's oceans and is also crucial to identifying hotspots for targeted cleanup efforts. Currently, the most common method to quantify marine plastic is using manta trawls for manual sampling. However, this method is cost-intensive and requires human labor. This study removes the need for manual sampling by using an autonomous method using neural networks and computer vision models, which trained on images captured from various layers of the ocean column to perform real-time plastic quantification. The best performing model has a Mean Average Precision of 85% and an F1-Score of 0.89 while maintaining near real-time processing speeds ~2 ms/img.
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Efficient Object Detection of Marine Debris using Pruned YOLO Model
Channel-pruned YOLOv4 raises detection speed on Trash-ICRA 19 from 15.19 to 19.4 FPS while keeping mAP near 96%, which the authors propose as an efficient model for marine debris detection on low-power hardware.