Abstract
Object detection on real-time edge devices for new applications with no or a limited amount of annotated labels is difficult. Where traditional data-hungry methods fail, transfer learning can provide a solution by transferring knowledge from a source domain to the target application domain. We explore domain adaptation techniques on a one-stage detection architecture, i.e. YOLOv3, which enables use on edge devices. Existing methods in domain adaptation with deep learning for object detection, use two-stage detectors like Faster-RCNN with adversarial adaptation. By using a one-stage detector, the speed increases by a factor of eight. With our proposed method, we reduce by \(28\%\) the changes in performance introduced by the gap between the source and target domains.
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Billast, M., De Schepper, T., Mets, K., Hellinckx, P., Oramas, J., Latré, S. (2022). Object Detection with Semi-supervised Adversarial Domain Adaptation for Real-Time Edge Devices. In: Leiva, L.A., Pruski, C., Markovich, R., Najjar, A., Schommer, C. (eds) Artificial Intelligence and Machine Learning. BNAIC/Benelearn 2021. Communications in Computer and Information Science, vol 1530. Springer, Cham. https://doi.org/10.1007/978-3-030-93842-0_5
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