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Automated Detection of Mine-Like Objects in Side Scan Sonar Imagery /


The task of detecting mine-like objects (MLOs) in side scan sonar imagery has a profound impact on military operations. The current process involves subject matter experts analyzing sonar images searching for MLOs. The automation of this problem has been heavily researched over the years without a definitive solution that outperforms the manual approach in real world scenarios. This paper presents a series of approaches and experiments centered on the use of GentleBoost feature selection classifiers for the detection of MLOs in side scan sonar. In a comparison of semi-synthetic versus real world training data with two different boosted single-feature selection classifiers, we see that semi-synthetic data can provide insight in to potential performance of a classifier. We run experiments training and testing GentleBoost single-feature classifiers on six different feature types, finding that the Haar-like feature classifier performs the best. We propose a GentleBoost multi-feature selection framework that allows for multiple feature types to be in the pool of selectable features, finding that a combination of Haar-like features, speeded up robust features (SURF), and simple shadow features performs better than the Haar-like feature classifier. Experiments with tiered, or cascaded, classifiers show a reduction in false positives for lower true positive rates. The multiple instance learning (MIL) approach shows great potential for future efforts, achieving improved true positive rates at higher false positive rates. A final approach considers the complimentary benefits of computer vision and human vision, introducing two brain- computer interface (BCI) systems. One BCI uses the Haar- like feature classifier as a first stage cascaded in to a human processing second stage. The other adds a third stage that employs a novel support vector machine (SVM) classifier based on the Haar-like feature and human interest scores from multiple subjects. Overall, our GentleBoost feature selection classifier variations result in performance improvement for the detection of MLOs in side scan sonar imagery

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