Noise2Seg: Automatic Few-Shot Selection from Noisy Web Data for Underwater Tropical Fishes Segmentation

We present a novel automatic few-shot selection (FSS) approach with a noisy web-collected dataset for underwater tropical fishes. Underwater image segmentation is of utmost importance in marine biology and underwater robotics. However, obtaining high-quality annotated underwater images is a challenging task due to the scarcity of underwater imaging systems and the high cost of data acquisition. Alternatively, web data is a readily available source of images, but it frequently contains web-corrupted noisy labels, making it challenging to curate a clean dataset for underwater tropical fish segmentation. Generally, the manual selection of high-quality images from web data requires human supervision and expert proofreading, which is both expensive and time-consuming. To address this issue, we propose Noise2Seg, an automatic processing framework composed by: (i) an automated web scrapping tool, (ii) a robust and automatic FSS process using a loss ranking scheme; (iii) a manual annotation component using 'Roboflow', (iv) the latest You Only Look Once version 8 (YOLOv8) for underwater fish detection and segmentation. Additionally, we curated and annotated a novel dataset for the segmentation of tropical fishes from Qatar marine ecosystem (Qatar Tropical Fishes-10 QTF-10). We compared the performance of manual and automatic FSS using mean average precision (mAP) score; manual FSS achieved all mAP (91.5%, 93.4%), and minimum mAP (19.5%, 54.8%), while automatic FSS achieved all mAP (92%, 99.5%), and minimum mAP (24.9%, 99.5%) with 5 and 10 images per class, respectively. The code and dataset related to this paper can be found on GitHub11https://github.com/GilalNauman/Automatic-Few-shot-Selction-ISNCC/tree/main, accessed on 1st of March 2023.