<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，无人载具在水下领域的应用愈发普及，因其可通过将多数任务中的人类参与度降至最低，大幅提升作业可靠性。环境感知对于水下作业的安全及制导、轨迹控制等任务至关重要。水雷探测是风险最高的作业类型之一，因为其所涉及的系统若采用人工操作模式，极易损坏载具并危及人员生命。从侧扫声呐图像中实现水雷探测自动化，可在提升作业安全性的同时降低假阳性与假阴性率。本次采集的数据集包含2010至2021年间使用Teledyne Marine公司Gavia自主水下航行器（Autonomous Underwater Vehicle，AUV）采集的1170幅真实声呐图像，该数据集包含充足信息，可将其中的目标对象分类为非水雷类海底目标（Non-Mine-like BOttom Objects，NOMBO）与类水雷接触目标（MIne-Like COntacts，MILCO）。该数据集已完成标注，可快速部署于目标检测、分类或图像分割任务。采集此类数据集需耗费大量时间与成本，这也提升了其稀缺性以及对科研与工业发展的重要价值。