Augmenting Coral Reef Monitoring with an Enhanced Detection System
Md Modasshir, Ioannis Rekleitis
In IEEE International Conference on Robotics and Automation 2020.
Abstract
Coral species detection underwater is a challenging problem. There are many cases when even the experts (marine biologists) fail to recognize corals, hence limiting ground truth annotation for training a robust detection system. Identifying coral species is fundamental for enabling the monitoring of coral reefs, a task currently performed by humans, which can be automated with the use of underwater robots. By employing temporal cues using a tracker on a high confidence prediction by a convolutional neural network-based object detector, we augment the collected dataset for the retraining of the object detector. However, using trackers to extract examples also introduces hard or mislabelled samples, which is counterproductive and will deteriorate the performance of the detector. In this work, we show that employing a simple deep neural network to filter out hard or mislabelled samples can help regulate sample extraction. We empirically evaluate our approach in a coral object dataset, collected via an Autonomous Underwater Vehicle (AUV) and human divers, that shows the benefit of incorporating extracted examples obtained from tracking. This work also demonstrates how controlling sample generation by tracking using a simple deep neural network can further improve an object detector.
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BibTeX
@inproceedings{ModasshirICRA2020,
author = {Md Modasshir and Ioannis Rekleitis},
title = {Augmenting Coral Reef Monitoring with an Enhanced
Detection System},
booktitle = {IEEE International Conference on Robotics and Automation},
year = {2020},
pages = {1874--1880},
address = {Paris, France},
doi = {10.1109/ICRA40945.2020.9196528}
}
Wed Apr 17 06:21:03 EDT 2024