<i>Side-scan sonar imaging for Mine detection</i>

Unmanned vehicles have become increasingly popular in the underwater domain in the last decade, as they provide better operation reliability by minimizing human involvement in most tasks. Perception of the environment is crucial for safety and other tasks, such as guidance and trajectory control, mainly when operating underwater. Mine detection is one of the riskiest operations since it involves systems that can easily damage vehicles and endanger human lives if manned. Automating mine detection from side-scan sonar images enhances safety while reducing false positives and negatives. The collected dataset contains 1170 real sonar images taken between 2010 and 2021 using a Teledyne Marine <i>Gavia</i> Autonomous Underwater Vehicle (AUV), which includes enough information to classify its content objects as NOn-Mine-like BOttom Objects (NOMBO) and MIne-Like COntacts (MILCO). The dataset is already annotated and can be quickly deployed for object detection, classification, or image segmentation tasks. Collecting a dataset of this type requires a significant amount of time and cost, which increases its rarity and relevance to research and industrial development.

近十年来，无人平台在水下领域的应用愈发普及，因其可通过减少人类在多数任务中的参与度，显著提升作业可靠性。环境感知对于水下作业的安全保障及导航、轨迹控制等核心任务至关重要。水雷探测是风险最高的作业之一：该任务涉及的系统一旦出现失误，极易损毁载具，若为载人作业则会直接危及人员生命安全。基于侧扫声呐图像实现水雷探测自动化，可在提升作业安全性的同时，有效降低假阳性与假阴性检出率。本次采集的数据集包含1170张真实声呐图像，采集时间跨度为2010年至2021年，采集设备为Teledyne Marine公司的Gavia自主水下航行器（Autonomous Underwater Vehicle，简称AUV）。该数据集涵盖充足的标注信息，可将图像中的目标对象分为非水雷类海底目标（NOn-Mine-like BOttom Objects，缩写为NOMBO）与类水雷接触目标（MIne-Like COntacts，缩写为MILCO）两类。本数据集已完成标注，可快速部署用于目标检测、分类或图像分割等任务。此类数据集的采集需要耗费大量时间与资金成本，因此其稀缺性与科研及工业开发的应用价值均较为突出。