A Generative Approach Towards Improved Robotic Detection of Marine Litter
By Jungseok Hong, Michael Fulton, Junaed Sattar. 2019
26 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oceanography, Robotics & Control
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Citations in the corpus
- TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris (2020, methodology): “ate-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]. TrashCan is a next step into autonomous detection and removal of trash: a larger, more detailed”