To avoid the underwater mine threat and to limit human interventions, navies use autonomous underwater vehicles. An underwater mine warfare mission is divided into four steps : object detection, classification, identification and neutralization. This PhD thesis proposes algorithmic solutions for the identification step done with a video camera. Thanks to the detection step, the identification vehicle knows approximately the object position. First, the vehicle has to detect and position this object exactly. Then it will be classified and identified. The underwater medium affects the images acquired with a video camera through absorption and scattering. The first step of our algorithm is to preprocess the images to help the detection and recognition (classification and identification) steps.We have proposed two detection methods. The first one consists in modifying image spectrum in order to obtain an image in which we will be able to detect edges of objects. The second method, based on region segmentation, has been developed from background subtraction methods. The background image has been learned at the beginning of the video when there is no object. The results of the latter have been compared to those obtained with a state-of-art method, on data acquired at sea. Once we have detected an object, we want to recognize it. For that, we use the correlation technique. The reference images have been obtained from 3D computer generated images of mines. This novel approach gives promising results. For each developed method, we have optimized the results through the use of navigational information. Indeed, depending on vehicle's motion, we can set constraints to improve the detection step and reduce processing time.
