# Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection

Source: [Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection](<https://www.sota2.com/research/paper/68fa727d1e209131cea56b8e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:27:04.021Z

Authors: Martin Aubard, L\\'aszl\\'o Antal, Ana Madureira, Erika \\'Abrah\\'am

Published: 2024-03-14T12:03:28.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.09313>)

[PDF](<https://arxiv.org/pdf/2403.09313.pdf>)