An iterative labeling method for annotating marine life imagery - Zhang, Zhiyong; Kaveti, Pushyami; Singh, Hanumant; Powell, Abigail; Fruh, Erica; Clarke, M. Elizabeth (Frontiers in Marine Science, 2023)
This paper presents a labeling methodology for marine life data using a weakly supervised learning framework by iteratively training a deep learning model using non-expert labels from crowdsourcing. It allows us to converge on a labeled image dataset via multiple training and production loops that exploit crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset comprises 10,505 images that were point annotated by NOAA biologists. This dataset allows us to validate the accuracy of our labeling process. We also apply our algorithm and methodology to a second dataset that consists of 3,968 completely unlabeled images. These image categories are challenging to label, such as sponges. Qualitatively, our results indicate that training with a tiny subset and iterating on those results allows us to converge to a large, highly annotated dataset with a small number of iterations. To demonstrate the effectiveness of our methodology quantitatively, we tabulate the mean average precision(mAP) of the model as the number of iterations increases.