Sonar Image Target Detection Based on Deep Learning

Due to the nonlinearity of the imaging of sonar equipment and the complexity of the underwater sound field environment, the gray level of the target area of the acquired underwater sonar image is relatively small. These characteristics are the target of the subsequent sonar image. Work such as detection and location tracking has brought great challenges. It has brought great challenges to solving the work of positioning and tracking, which makes the research of sonar image target detection based on deep learning very important. This article aims at studying the use of sonar to detect image targets based on deep learning technology. This article proposes a variety of sound image denoising methods based on multiresolution tools. The purpose of this article is to divide the natural image into blocks at an appropriate rate according to the change of the sampling matrix and measure the underwater natural image. The sound image defines an information model. These methods have greatly changed the image and period of using remote and temporary information. The translation results of these methods are all valid. The sharpening separation method based on filtered image and bidirectional detection should be published through a solution algorithm and different frames, and the expected algorithm can be reused and extracted as an action to improve the similarity of the image and should be saved and separated in detail. The result is correct. This article studies the application of deep learning methods in sonar image target detection and designs corresponding algorithms for improvement and functional realization in view of the current deficiencies and needs in this field. The experimental results show that the improved scheme and applied algorithm proposed in this article can achieve good results, the verification sample set includes 184 remote-sensing aircraft targets, and the resolution of remote-sensing images is unified to 1644 × 971 size. The accuracy of the target detection algorithm has been significantly improved, reaching 74.6%, and the detection speed has also been greatly improved. Compared with the RNN algorithm, the speed has been increased by 7 times. The detection results confirmed that the improved algorithm has higher positioning accuracy and faster detection speed.