Résumé
Whether in times of conflict or during peacetime, the fight against underwater mines is an activity of paramount importance. For naval forces, it is a question of acquiring the means to eliminate the possible presence of underwater mines near the domestic coasts or in areas of amphibious and naval operations. For the civilian field, the offshore wind in northern Europe is the example of a sector particularly affected by the threat of unexploded ordnances (UXO) dating from military conflicts of the past. The advances of recent decades in marine technologies have enabled the development of high-resolution acoustic imaging systems fitted on towed fish or autonomous underwater vehicle (AUV), capable of generating high-resolution imagery of the underwater environment. Based on deep learning approaches, the collected data is used to develop Automatic Target Recognition (ATR) algorithms to detect suspicious objects on the seafloor and classify each as an object of interest (e.g., a mine) or not. However, because obtaining labelled underwater images demands time and effort, applying deep learning based approaches in underwater environment remains a challenge due to the scarcity of training data. This paper presents a new approach for improving the detection of minelike objects. Our method is based on two steps: the first step consists in transforming our detection problem into a semantic segmentation task. The second step involves active learning. We demonstrate that our approach leads to a significant improvement in terms of area under the curve (AUC).