Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional Neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94 recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.
Automatic target recognition (ATR) for unexploded ordnance (UXO) detection and classification using sonar data of opportunity from open oceans survey sites is an open research area. The goal here is to develop ATR spanning real-aperture and synthetic aperture sonar imagery. The classical paradigm of anomaly detection in images breaks down in cluttered and noisy environments. In this work we present an upgraded and ultimately more robust approach to object detection and classification in image sensor data. In this approach, object detection is performed using an in-situ weighted highlight-shadow detector; features are generated on geometric moments in the imaging domain; and finally, classification is performed using an Ada-boosted decision tree classifier. These techniques are demonstrated on simulated real aperture sonar data with varying noise levels. A new algorithm for the detection of underwater man-made objects in sonar imagery is proposed. The algorithm is made extremely fast by employing a cascaded architecture and by exploiting integral-image representations. As a result, the method makes real-time detection of objects of interest in streaming sonar data collected by an autonomous underwater vehicle feasible. No training data is required because the proposed method is adaptively tailored to the environmental characteristics of the sensed data that is collected in situ. The flexible yet rigorous approach also addresses and overcomes five major limitations that plague the most popular detection algorithms that are in common use. The power and utility of the proposed approach is demonstrated on a large, challenging data set of synthetic aperture sonar imagery collected at sea. Underwater chain cleaning and inspection tasks are costly and time consuming operations that must be performed periodically to guarantee the safety of the moorings. We propose a framework towards an efficient and cost-effective solution by using an autonomous underwater vehicle equipped with a forward-looking sonar. As a first step, we tackle the problem of individual chain link detection from the challenging forward-looking sonar data. To cope with occlusions and intensity variations due to viewpoint changes, the recognition problem is addressed as local pattern matching of the different link parts. We exploit the high frame-rate of the sonar to improve, by registration, the signal-to-noise ratio of the individual sonar frames and to cluster the local detections over time to increase robustness. Experiments with sonar images of a real chain are reported, showing a high percentage of correct link detections with good accuracy while potentially keeping real-time capabilities. The problem of automatic detection and classification for mine hunting applications is addressed. We propose a set of algorithms which are tested using a large database of real synthetic aperture sonar (SAS) images. The highlights and shadows of the objects in an SAS image are segmented using both a Markovian algorithm and the active contours algorithm. The comparison of both segmentation results is used as a feature for classification. In addition, other features are considered. These include geometrical shape descriptors, not only of the shadow region, but also of the object highlight, which demonstrates a significant improvement of the performance. Furthermore, a novel set of features based on the image statistics is described. Finally, we propose an optimal feature set that leads to the best classification results for the available database. Detection of underwater objects is a critical task for a variety of underwater applications (off-shore, archeology, marine science, mine detection). This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the missions and near real time update of the operator. In this paper we propose a new method for object detection in sonar imagery capable of processing images extremely rapidly based on the Viola and Jones boosted classifiers cascade. Unlike most previously proposed approaches based on a model of the target, our method is based on in-situ learning of the target responses and of the local clutter. Learning the clutter is vitally important in complex terrains to obtain low false alarm rates while achieving high detection accuracy. Results obtained on real and synthetic images on a variety of challenging terrains are presented to show the discriminative power of such an approach. A method for classifying objects in sonar imagery is proposed. Motivated by the high-resolution achievable by modern imaging sonars, a novel template matching technique is developed that compares a target signature generated from a simple acoustic model with the actual image of an object being classified. The approach uses both the correlation with target echoes as well as projected acoustic shadow, and is tested on data obtained from a synthetic aperture sonar during experiments at sea. It is compared to two commonly used methods that are based on normalized cross-correlation, and results show that the proposed method outperforms the standard methods in terms of receiver-operating characteristic (ROC) curves as well as confusion matrices. The majority of existing automatic mine detection algorithms which have been developed are robust at detecting mine-like objects (MLOs) at the expense of detecting many false alarms. These objects must later be classified as mine or not-mine. The authors present a model based technique using Dempster–Shafer information theory to extend the standard mine not-mine classification procedure to provide both shape and size information on the object. A sonar simulator is used to produce synthetic realisations of mine-like object shadow regions which are compared to those of the unknown object using the Hausdorff distance. This measurement is fused with other available information from the object's shadow and highlight regions to produce a membership function for each of the considered object classes. Dempster–Shafer information theory is used to classify the objects using both mono-view and multi-view analysis. In both cases, results are presented on real data.