A Generative Approach Towards Improved Robotic Detection of Marine Litter
This addresses the problem of limited training data for underwater trash detection, which is incremental as it applies existing generative methods to a specific domain.
The paper tackles data scarcity in underwater image datasets for marine debris detection by using a two-stage VAE to generate synthetic images, which are then selected by a binary classifier for dataset augmentation. The result shows that an object detector trained on augmented data outperforms one trained only on real data.
This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism. From the images generated by the two-stage VAE, the binary classifier selects "good quality" images and augments the given dataset with them. Lastly, a multi-class classifier is used to evaluate the impact of the augmentation process by measuring the accuracy of an object detector trained on combinations of real and generated trash images. Our results show that the classifier trained with the augmented data outperforms the one trained only with the real data. This approach will not only be valid for the underwater trash classification problem presented in this paper, but it will also be useful for any data-dependent task for which collecting more images is challenging or infeasible.