用便宜的声呐检测水下障碍物,验证低成本方案可行性
Exploring the Feasibility of Affordable Sonar Technology: Object Detection in Underwater Environments Using the Ping 360
- 用人工标注数据训练U-Net模型,提升声呐图像分割能力
- 在复杂环境中性能受限,需大量预处理和标注才能有效识别
- 首次评估低成本声呐在复杂水下目标检测中的潜力
本研究探索了主要用于导航的低成本Ping 360声呐设备在检测复杂水下障碍物方面的可行性。其核心动机在于该设备价格低廉且开源,可作为昂贵成像声呐的替代方案。研究在受控环境中考察了表面反射和物体阴影对声呐成像的影响,并构建了一个手动标注的声呐图像数据集,用于训练U-Net分割模型。结果表明,尽管在简单场景中表现良好,但在杂乱或强反射环境中,若不进行大量数据预处理与标注,性能显著下降。据我们所知,这是首个系统评估Ping 360在复杂目标检测中应用的研究。该工作为未来基于AI的低预算声呐解读提供了关键参考。
原文摘要 · Abstract (English)
This study explores the potential of the Ping 360 sonar device, primarily used for navigation, in detecting complex underwater obstacles. The key motivation behind this research is the device's affordability and open-source nature, offering a cost-effective alternative to more expensive imaging sonar systems. The investigation focuses on understanding the behaviour of the Ping 360 in controlled environments and assessing its suitability for object detection, particularly in scenarios where human operators are unavailable for inspecting offshore structures in shallow waters. Through a series of carefully designed experiments, we examined the effects of surface reflections and object shadows in shallow underwater environments. Additionally, we developed a manually annotated sonar image dataset to train a U-Net segmentation model. Our findings indicate that while the Ping 360 sonar demonstrates potential in simpler settings, its performance is limited in more cluttered or reflective environments unless extensive data pre-processing and annotation are applied. To our knowledge, this is the first study to evaluate the Ping 360's capabilities for complex object detection. By investigating the feasibility of low-cost sonar devices, this research provides valuable insights into their limitations and potential for future AI-based interpretation, marking a unique contribution to the field.
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