Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection
Martin Aubard László Antal Ana Madureira Erika Ábrahám
In this paper we present YOLOX-ViT, a novel object detection model, and
investigate the efficacy of knowledge distillation for model size reduction
without sacrificing performance. Focused on underwater robotics, our research
addresses key questions about the viability of smaller models and the impact of
the visual transformer layer in YOLOX. Furthermore, we introduce a new
side-scan sonar image dataset, and use it to evaluate our object detector's
performance. Results show that knowledge distillation effectively reduces false
positives in wall detection. Additionally, the introduced visual transformer
layer significantly improves object detection accuracy in the underwater
environment. The source code of the knowledge distillation in the YOLOX-ViT is
at https://github.com/remaro-network/KD-YOLOX-ViT.