Hardware efficient underwater mine detection and classification

Detection and classification of mine-like objects in side-scan sonar images needs to compensate for variability of objects, noise and background signatures. The unsupervised algorithm presented in this paper addresses improvements with respect to previous work and focuses on object and shadow detection based on morphological operators. Feature extraction from the detected objects and their classification into two classes, namely mine or non-mine like objects is described. Row-wise processing technique is applied for decreasing computational costs and memory usage to allow easy porting of the algorithm to an embedded architecture. The performance of the algorithms is measured against the obtained ground-truth.