Abstract
This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks.
Highlights
Side scan sonar (SSS), among the most common sensors used in ocean survey, can provide images of the seafloor and underwater target
In order to ensure that these models can achieve real-time processing for on-board applications, the experimental tests concerning the prediction time were performed on an embedded platform, NVIDIA Jetson AGX Xavier
We compared different models’ performances, using four common performance metrics [20]: Pixel accuracy (Pixel Acc.), measuring the proportion of pixels accurately predicted to the total pixels, mean accuracy (Mean Acc.) which is the average of the prediction accuracy over all categories, mean intersection over union (IU), and frequency weighted IU
Summary
Side scan sonar (SSS), among the most common sensors used in ocean survey, can provide images of the seafloor and underwater target. Various methods for SSS image segmentation have been proposed, most of which are based on unsupervised segmentation methods, such as active contour model, clustering segmentation method and Markov random field (MRF) segmentation method, etc. The common techniques of SSS image segmentation include the clustering segmentation method and the Markov random field (MRF) segmentation method. Celik T. et al [6] utilized clustering algorithm for SSS image segmentation. In their algorithm, the multiresolution representation of the input image was constructed using the undecimated discrete wavelet transform (UDWT), where the feature vectors were extracted. The model which performed the best on the validation set was obtained
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