A Study on the Robustness of Object Detectors in Aqua-Farming

Rajarshi Biswas, Om Khairate, Mohamed Salman, Dirk Werth

2025

Abstract

In this paper, we study the robustness of state-of-the-art object detectors under transfer learning to detect live fishes swimming inside a fish tank. To overcome data limitations, we perform experiments in which we train these detectors with small amounts of annotated data and observe their robustness on out-of-domain data while tracking performance on in-domain test data. We compare YOLOv8l, RTMDet, RT-DETR, SSD-MobileNet and Faster-RCNN for performing dense object detection on images of fish schools obtained from an aqua-farm and observe their robustness on out-of-domain data from the MS COCO, ImageNet, and Pascal VOC datasets respectively. On the in-domain test set, we achieved the highest detection accuracy of 0.896 mAP with bounding boxes and 0.9214 mAP with instance masks using the YOLOv8l model. However, the same model exhibits a false positive rate of 55.77% on out-of-domain data from the MS COCO dataset. To mitigate false positive prediction we studied two different strategies, (1) re-training the models incorporating out-of-domain data and (2) re-training models by updating only the biases. We found that incorporating out-of-domain data to train the models leads to the highest reduction in false positive detection, however, this does not guarantee steady and high performance on the in-domain test data.

Paper Citation

in Harvard Style

Biswas R., Khairate O., Salman M. and Werth D. (2025). A Study on the Robustness of Object Detectors in Aqua-Farming. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 814-822. DOI: 10.5220/0013342200003905

in Bibtex Style

@conference{icpram25,
author={Rajarshi Biswas and Om Khairate and Mohamed Salman and Dirk Werth},
title={A Study on the Robustness of Object Detectors in Aqua-Farming},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={814-822},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013342200003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A Study on the Robustness of Object Detectors in Aqua-Farming
SN - 978-989-758-730-6
AU - Biswas R.
AU - Khairate O.
AU - Salman M.
AU - Werth D.
PY - 2025
SP - 814
EP - 822
DO - 10.5220/0013342200003905
PB - SciTePress