Robust object detection in sonar images is an important task for underwater exploration, navigation and mapping. Current methods make assumptions about the shape, highlight or shadow of an object, which may be invalid for some environments or targets. We focus on the area of feature extraction-based detection, which does not rely on information about the shape of the target, towards a robust framework for target detection for a variety of seabed structures and target types. The proposed framework first estimates the seabed type from the spatial distribution of features to determine the set of optimal parameters, and then obtains a set of features which are filtered according to intensity and distribution to yield a detection decision. The proposed method also provides a means to determine the seabed type, and a machine-learning based methodology to choose the feature detectors' parameters to match the evaluated seabed type. We report the performance of a variety of feature detectors for a simulated environment and of one feature detector for real sonar images. Results show the importance of choosing the parameters of the feature extractors based on the current environmental conditions and the proposed method obtains a favourable tradeoff between detection and false alarm rates.

中文翻译：

---

使用声纳图像功能进行目标检测

声纳图像中的强大目标检测是水下勘探，导航和制图的重要任务。当前的方法对对象的形状，高光或阴影做出假设，这对于某些环境或目标可能是无效的。我们专注于基于特征提取的检测领域，该领域不依赖于有关目标形状的信息，而是针对各种海底结构和目标类型的强大目标检测框架。提出的框架首先根据特征的空间分布估算海床类型，以确定最佳参数集，然后获得根据强度和分布进行过滤的一组特征，以得出检测决策。所提出的方法还提供了一种确定海床类型的方法，以及基于机器学习的方法来选择特征检测器的参数以匹配评估的海床类型。我们报告了针对模拟环境的各种特征检测器的性能以及针对真实声纳图像的一种特征检测器的性能。结果表明，基于当前环境条件选择特征提取器参数的重要性，并且所提出的方法在检测率与虚警率之间取得了良好的折衷。