Underwater trash Detection with Deep Learning

What is the problem with underwater trash?

Sea animals such as fish, turtles, seabirds, and seals may get stuck in the underwater trash, which restricts their movement and even causes wounds on their bodies. It is also possible that they confuse the  trash with food and eat  it, leading to illness or damage to their internal organs or even resulting in their death.

It should be considered that these problems are not just threatening the aquatic world; other animals and humans who consume seafood are also prone to sickness due to polluted seafood.

Undoubtedly, the issues mentioned above will only get worse unless we take fast and effective actions to solve the problem.

The first step: Dataset

The first step for training deep learning models is collecting data or using an available dataset. This article uses the “TrashCan 1.0: An Instance-Segmentation Labeled Dataset of Trash Observations”. This dataset was curated and published in 2020 by the Japan Agency of Marine-Earth Science and Technology (JAMSTEC). The TrashCan dataset has 7,212 annotated images and contains 22 classes, including a wide variety of undersea flora and fauna, ROV, and trash. However, for simplicity, we will only use five classes in this project.

Since we are using a public dataset we can go ahead and upload the dataset to the AIEX platform.  If we were to start our own dataset, we could use the platform’s search engine to find images and annotate them.

The second step: Training

After uploading the dataset (this article explains how to upload the dataset), we should verify all annotations and then set the state of all images to “ready to train”. Now we click on “Build new version” to split images into the train, validate and test datasets.

Now we need to select a model and train it to identify segment trash in images. In the AIEX platform, we can select segmentation models from several frameworks, including PyTorch, TensorFlow, and TAO.

Table 1. Training results on the validation dataset.

The last step: Deploy

The inference process can determine how well the model has been trained. Here are some of our results.