Acoustic obstacle detection for safe auv surfacing

We propose an automatic sea surface object detection from forward looking sonar images. The considered sea surface obstacles are man-made objects: buoys, boats, ships (motorboats or sailboats). Their acoustic signature varies according to their type and state (fixed or moving). The proposed detection scheme is hierarchical in order to manage the various target signatures. The first step consists in detecting stationary self noise from ships. In case of detection, the strong-intensity strip corresponding to the ship direction is removed to avoid ship noise disturbance during other target detection processes. The next step consists in detecting the other types of obstacles. It is based on an adaptive CFAR (Constant False Alarm Rate) thresholding. The final step consists in analyzing the area around every detected position in order to state that this latter is a reliable obstacle and not a wake signature. Promising results are obtained using real data collected at sea with various objects and scenarios.