Detection of Marine Debris

Marine litter poses a growing threat to the health of our planet. Our planet's oceans and lakes are choked with plastic and other debris, fed to them through equally polluted rivers and streams, carried in by runoff, or dropped off ships. The pollution of these water sources can devastate entire populations of aquatic, terrestrial, and plant life. While preventing debris from entering the water at all is the only sustainable solution to this problem, clean up efforts are crucial to solving the problem or at least slowing its spread. 

To aid in the application of robots to cleanup efforts, we have compared four popular deep neural networks for object detection, trained to detect trash using data drawn from a dataset of underwater ROV dive videos which can be found here.

A video of our results is included below, along with tables comparing the performance and accuracy of all four networks, trained by fine tuning.


Detection metrics

Network mAP Avg. IOU plastic bio rov
YOLOv2 47.9 54.7 82.3 9.5 52.1
Tiny-YOLO 31.6 49.8 70.3 4.2 20.5
Faster-RCNN 81.0 60.6 83.3 73.2 71.3
SSD 67.4 53.0 69.8 6.2 55.9


Performance Metrics (in frames per second)

Network 1080 TX2 CPU
YOLOv2 74 6.2 0.11
Tiny-YOLO 205 20.5 0.52
Faster-RCNN 18.75 5.66 0.97
SSD 25.2 11.25 3.19


A newer addition to the work is the evaluation of transfer learning as a method to overcome the limited amount of data available for this problem. YOLOv2 was trained using fine tuning, transfer learning on the last four and last three layers only. 

Detection metrics for YOLOv2

Training Method mAP Avg. IOU plastic bio rov
Fine Tuning 47.9 54.7 82.3 9.5 52.1
Last 4 Layers 33.9 45.5 71.3 13.6 17.0
Last 3 Layer 39.5 34.1 74.6 19.9 23.9


More information on the specifics of each class and the construction of the data model can be found in the relevant papers on the Publications page.