Target detection algorithm in side-scan sonar image based on pixel importance measurement

The prior task is to stabilize the detection ability and reduce false alarm probability simultaneously underlow signal-noise ratio condition. A two-stage novel pixel importance measurement algorithm in the side-scan sonar detection application is proposed. In first stage of the algorithm, a new feature defined as Pixel Importance Value(PIV) is proposed based on distances between the target pixel and each other pixels. PIV measurement of current pixel is defined as the weighted sum of all remaining segmented pixels. The weighted part refers to Gaussian kernel, which means closer pixels gets higher weight. Thus, targets with higher pixel importance value can be located. In the second stage, we use convolutional neural network as classifier to eliminate the dot-like false targets in terms of targets shape. In our experiments based on recent underwater data obtained by autonomous underwater vehicle, we demonstrate superior performance of our algorithm over the state-of-the-art sonar detection algorithms in terms of 90.39% recall rate and 2.39% false alarm probability.