Robotic Detection of Marine Litter Using Deep Visual Detection Models

By Michael Fulton, Jungseok Hong, M. Islam, Junaed Sattar. 2018

199 citations recorded by the source corpus.

Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. This paper evaluates a number of deep-learning algorithms performing the task of visually detecting trash in realistic underwater environments, with the eventual goal of exploration, mapping, and extraction of such debris by using AUVs. A large and publicly-available dataset of actual debris in open-water locations is annotated for training a number of convolutional neural network architectures for object detection. The trained networks are then evaluated on a set of images from other portions of that dataset, providing insight into approaches for developing the detection capabilities of an AUV for underwater trash removal. In addition, the evaluation is performed on three different platforms of varying processing power, which serves to assess these algorithms’ fitness for real-time applications.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering, Pollution, Water Science and Technology, Robotics & Control

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Citations in the corpus

TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris (2020, methodology): “More recently, we presented an initial evaluation of state-of-the-art object detection algorithms for trash detection, using a predecessor of TrashCan [8], and showed how such datasets could be effectively improved with data produced by generative models [9].”
A Generative Approach Towards Improved Robotic Detection of Marine Litter (2019, methodology): “As an example, visual detection of underwater trash [5] by autonomous underwater vehicles (AUVs) suffers from lower accuracy compared to the performance of the same deep-learned detectors in non-underwater object detection tasks.”
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters (2021, background): “Based on these datasets, works have been done for vision-based waste detection [14, 10, 18].”