With 3D imaging of the multisonar beam and serious interference of image noise, detecting objects based only on manual operation is inefficient and also not conducive to data storage and maintenance. In this paper, a set of sonar image automatic detection technologies based on 3D imaging is developed to satisfy the actual requirements in sonar image detection. Firstly, preprocessing was conducted to alleviate the noise and then the approximate position of object was obtained by calculating the signal-to-noise ratio of each target. Secondly, the separation of water bodies and strata is realized by maximum variance between clusters (OTSU) since there exist obvious differences between these two areas. Thus image segmentation can be easily implemented on both. Finally, the feature extraction is carried out, and the multidimensional Bayesian classification model is established to do classification. Experimental results show that the sonar-image-detection technology can effectively detect the target and meet the requirements of practical applications.
In the Second World War, the US Navy successfully escaped the Japanese seabed minefield by using the sonar equipment. Subsequently, many countries have begun to pay attention to the development of sonar technology, especially in the military field. With the increasing marine development, the exploration of the ocean was limited not only to military purposes but also to commercial and civilian purposes, such as submarine resource development, oil exploration, automatic mapping of submarine topography, and detection of fish stocks. In order to adapt to the underwater environment, intelligent underwater robot research has been carried out for the laying of submarine cables, underwater demining, and so on. As the current requirements for intelligent sonar equipment are getting higher and higher, now there are many underwater target recognition technology applications. Therefore, whether in the field of military or civilian, underwater target recognition technology will be one of the main technologies in the future of ship and ocean engineering to research.
Due to the complexity of the seabed environment, there are some problems such as poor contrast of the sonar image, the serious noise of the submarine reverberation, low resolution, strong interference, and less dark pixels [
Casselman et al. proposed a method of multilayer perceptual neural network detection with low false alarm rate and applied this method to the passive sonar detection [
The three-dimensional sonar data consists of three dimensions: navigation, distance, and beam. When vessels sail at sea, sonar is used to detect underwater objects. The direction of the sailing vessel is the navigational dimension of the sonar data. The distance from the sonar to the bottom of the sea is the distance dimension of the sonar data. The angle of the acoustic wave divergence in the underwater is the beam dimension of the sonar data. In addition, the distribution of the beam is uniform. Since the three-dimensional sonar data can obtain the information of the three-dimensional space object, the image is clearer and more visible than the current image sonar. Three-dimensional sonar data “
The composition of three-dimensional image data volume.
Sonar image preprocessing is the foundation of the sonar image processing and an indispensable part of image recognition [
Image denoising methods mainly include neighborhood averaging method, self-adaptive smooth filtering, wavelet transformation denoising method, and median filtering denoising. According to the characteristics of the noise, we can choose one or more targeted denoising methods. In this paper, through the experimental analysis and judgment, we choose the median filtering method to the sonar image processing. The median filter is a kind of nonlinear filters; it can eliminate noise and at the same time keep the detail of the image. The steps are as follows: Moving the template in turn in the image until the center of the template overlaps with a pixel in the image Extracting the gray value of the pixels which correspond with the template Aligning these value in order Assigning the intermediate value or the average of the two middle values to the central pixel in the corresponding template.
In order to eliminate the isolated noise points in the image, we can make the approximate values replace the pixel values that are significantly different from the surrounding pixels by using the median filter.
Bleaching process of sonar signal refers to the balanced background which can reduce the interference of noise to a certain extent so that the data will be easy to deal with for subsequent processing. According to the characteristics and properties of the sonar, choice of sonar signal bleaching processing formula is as follows:
The signal-to-noise ratio is the ratio of the power spectrum of the signal to the noise, but power spectra are usually hard to measure. Therefore we can use the ratio of signal variance to noise variance to approximate the signal-to-noise ratio. According to the characteristics of sonar image, we can use the following formula to calculate the target’s signal-to-noise ratio:
In the actual detection of the ocean, sonar needs not only to detect the object in the water but also to detect objects in the stratum. Due to the large differences between the water and stratum, the detection is much easier since there is less noise in the sea water, while the reverberation of stratum is relatively serious, which causes great difficultly in detecting the target. So we can separate the water and stratum and then process the sonar image in different regions, respectively.
In this paper, we use the OTSU algorithm based on the gray-level histogram to proceed image threshold segmentation. A good result of segmentation is obtained by using the corrosion expansion operator to process closure operation.
OTSU threshold segmentation method uses variance to find the best threshold value between the two kinds of pixels and uses variance between clusters to evaluate the segmentation results. The formula is as follows:
Image segmentation is an important part of sonar image processing [ The maximum grayscales value and the minimum grayscales value of the image are denoted as According to the threshold value A new threshold value If
Entropy is a function that describes the state of the system and represents the average amount of information. At the same time, entropy is used to calculate the disorder in a system phenomenon and can be used as a measure of the degree of chaos. When performing the evaluation of the segmentation effect, the formula is as follows:
In the sonar image processing, the two-dimensional maximum entropy is often used to divide the threshold to obtain the optimal threshold. The two-dimensional maximum entropy method uses the two-dimensional histogram of pixel intensity and regional gray mean and finds the optimal threshold according to the maximum entropy.
In fact, we need to not only detect the submarine target but also classify the target preliminary during the sonar sweeping. The main categories are cylindrical, spherical, cable-like targets. In this paper, feature extraction is used to obtain the characteristics of the target. After multiple experiments, the extracted length-width ratio, pixel value, and signal-to-noise ratio are analyzed to find that they conform to the Gaussian distribution. Therefore, this paper adopts a multidimensional Bayesian classification model [
Figure
Sonar image processing flow chart.
In order to verify the effectiveness and superiority of the proposed algorithm, this paper carries out simulation experiments on the three-dimensional imaging sonar data. The experimental data is all derived from Hangzhou Institute of Applied Acoustics. Firstly, check the noise suppression effect of sonar signal after bleaching process. Then the OTSU algorithm is used to separate the water and stratum so that the interface is found out. The image segmentation algorithm simulation experiment is carried out in the light of the characteristics of the sonar image of each part. The results of the two image segmentation methods are compared. The two methods are the iterative threshold method and the two-dimensional maximum entropy algorithm. Finally, the results of the classification are summarized by using the multidimensional Bayesian classification model.
Figure
The comparison of the sonar signals before and after bleaching.
Figure
The results of water and stratum separation using OTSU.
Figures
The comparison between the method we used and the two-dimensional maximum entropy.
For the object classification of the sonar image, the most commonly used methods are the K-nearest neighbor method and the multidimensional Bayesian classification model method. Although K-nearest neighbor method is simple and intelligible, its computational complexity is too high to apply to multiclassification tasks. In reality, submarine targets are different not only in shape but also in distribution. The multidimensional Bayesian classification model can accommodate small-scale data samples. At the same time, the multidimensional Bayesian classification model can adapt to multiclassification tasks and incremental training. Therefore, it is reasonable to use the multidimensional Bayesian classification model to classify the targets.
Figure
The classification result of the multidimensional Bayesian model and K-nearest neighbor method on a sonar dataset.
The characteristic distribution of columnar targets.
The characteristic distribution of spherical objects.
In this paper, we achieve the automatic detection technology of target in the sonar image based on three-dimensional imaging. This technology includes a complete set of processes such as sonar image preprocessing, water and stratum separation, image threshold segmentation, and target classification recognition. At the same time, a better sonar image processing result is achieved, which satisfies the actual processing requirements of the sonar image. Besides, this technology not only improves the efficiency of the sonar image target detection and reduces the unnecessary labor, but also improves accuracy of the sonar image target detection to a certain extent and creates a low false alarm rate and high detection rate of the sonar target detection technology. This technology has been applied in Hangzhou Institute of Applied Physics.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported by National Natural Science Foundation of China (Grant nos. 61671193 and 61102028), International Science & Technology Cooperation Program of China (Grant no. 2014DFG12570), and project funded by China Postdoctoral Science Foundation (Grant no. 2015M571878).