YOLOX-ViT中的知识蒸馏用于侧扫声纳目标检测
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection
AI总结：
本文提出YOLOX-ViT模型，通过知识蒸馏减小模型尺寸并提升侧扫声纳目标检测性能，引入视觉Transformer层显著提高水下检测精度，并发布新数据集。
摘要：
本文提出了YOLOX-ViT，一种新颖的目标检测模型，并研究了知识蒸馏在模型尺寸缩减而不牺牲性能方面的有效性。研究聚焦于水下机器人领域，解决了关于较小模型可行性和YOLOX中视觉Transformer层影响的关键问题。此外，我们引入了一个新的侧扫声纳图像数据集，并用它来评估我们的目标检测器的性能。结果表明，知识蒸馏有效减少了墙壁检测中的误报。此外，引入的视觉Transformer层显著提高了水下环境中的目标检测精度。YOLOX-ViT中知识蒸馏的源代码可在https://github.com/remaro-network/KD-YOLOX-ViT获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
