<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，无人水下平台在水下领域的应用愈发广泛，其通过降低绝大多数任务中的人工参与度，有效提升了作业可靠性。环境感知对于水下作业的安全保障、导航及轨迹控制等任务均至关重要。水雷探测是风险最高的作业类型之一，该作业涉及的系统若出现故障，极易损毁载具；若为载人作业，更会直接威胁人员生命安全。通过侧扫声呐图像实现水雷探测自动化，既可提升作业安全性，又能有效降低误报与漏报率。本次采集的数据集包含2010年至2021年间，使用Teledyne Marine公司Gavia型自主水下航行器（Autonomous Underwater Vehicle，AUV）获取的1170幅真实声呐图像。数据集包含充足信息，可将图像内的目标划分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）两类。该数据集已完成标注，可快速部署于目标检测、分类或图像分割相关任务中。采集此类数据集需要投入大量时间与成本，这也提升了其稀缺性，使其在科研与工业研发中更具应用价值。

搜集汇总

背景与挑战

背景概述

该数据集包含1170张已标注的侧扫声纳图像，用于水雷检测任务，采集自2010至2021年，适用于物体检测、分类和图像分割。数据集因其稀有性和研究价值而具有重要意义。