Authors:
Rajarshi Biswas
;
Om Khairate
;
Mohamed Salman
and
Dirk Werth
Affiliation:
August-Wilhelm Scheer Institute, Uni-Campus D 5 1, 66123 Saarbrücken, Germany
Keyword(s):
Deep Learning, Computer Vision, Industrial Application.
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 strateg
ies, (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.
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