A recurrent neural network for detecting objects in sequences of sector-scan sonar images

This work presents a system for detecting small man-made objects in sequences of sector-scan images formed using a medium-range sector-scan sonar. The detection of such objects is considered out to ranges of 200 m from the vessel and while the vessel is in motion. This paper extends previous work by making use of temporal information present in the data to improve performance. The system begins by cleaning the imagery, which is done by tracking objects on the sea bed in the imagery and using this information to obtain an improved estimate of the motion of the vessel. Once the vessel's motion is accurately known, the imagery is cleaned by temporally averaging the images after motion compensation. The detector consists of two stages. After the first detection stage has identified possible objects of interest, a bank of Kalman filters is used to track objects in the imagery and to supply sequences of feature vectors to the final detection stage. A recurrent neural network is used for the final detection stage. The feedback loops within the recurrent network allow the incorporation of temporal information into the detection process. The performance of the proposed system is shown to exceed the performances of other models for the final detection stage, including nonrecurrent networks that make use of temporal information supplied in the form of temporal feature vectors. The proposed detection system attains a probability of detection of 77.0% at a mean false-alarm rate of 0.4 per image.