Relation and Application Method of Deep Learning Sea Target Detection and Segmentation Algorithm

Guangfu Li,Zheng Wang,Jia Ren

doi:10.1155/2020/1847517

Abstract

Target detection and segmentation algorithms have long been one of the main research directions in the field of computer vision, especially in the study of sea surface image understanding, these two tasks often need to consider the collaborative work at the same time, which is very high for the computing processor performance requirements. This article aims to study the deep learning sea target detection and segmentation algorithm. This paper uses wavelet transform-based filtering method for speckle noise suppression, deep learning-based method for land masking, and the target detection part uses an improved CFAR cascade algorithm. Finally, the best separable features are selected to eliminate false alarms. In order to further illustrate the feasibility of the scheme, this paper uses measured data and simulation data to verify the scheme and discusses the effect of different signal-to-noise ratio, sea target type, and attitude on the algorithm performance. The research data show that the deep learning sea target detection and segmentation algorithm has good detection performance and is generally applicable to ship target detection of different types and different attitudes. The results show that the deep learning sea target detection and segmentation algorithm fully takes into account the irregular shape and texture of the interfering target detected in the optical remote sensing image so that the accuracy rate is 32.7% higher and the efficiency is increased by about 1.3 times. The deep learning sea target detection is compared with segmentation algorithm, and it has strong target characterization ability and can be applied to ship targets of different scales.

Highlights

Despite the rapid development of China’s HNA industry, the current severe sea search and rescue conditions and environment have made the existing search and rescue system’s emergency rescue capabilities facing severe tests
Sea surface target detection methods are diverse and can be divided into two major categories: the first category is image enhancement methods based on the spatial domain; the second category is image enhancement methods based on the transform domain
Based on the existing network model, this paper proposes a deep learning sea surface target detection and segmentation algorithm, and this method mainly uses the deep learning sea target detection and target detection model provided by Google and combines the superpixel segmentation algorithm to optimize the Grabcut algorithm, which realizes the deep learning sea target detection and segmentation algorithm, which can be accurate target contour and semantic information

Summary

Introduction

Despite the rapid development of China’s HNA industry, the current severe sea search and rescue conditions and environment have made the existing search and rescue system’s emergency rescue capabilities facing severe tests. China’s existing emergency search technology equipment is still difficult to effectively meet the needs of the rapid development of maritime transportation, and its ability to deal with major disasters and accidents at sea is weak, and there is a large gap compared with the international advanced level. China’s maritime search and rescue technology and equipment cannot meet the rapid search requirements for distress targets in the deep sea under harsh sea conditions. Sea surface target detection is based on specific purposes and needs, by enhancing the image contrast method to enhance specific information in the image, while reducing or removing unimportant or unnecessary information, thereby improving the overall image quality. In actual applications, depending on the specific application objects, occasions, and purposes, the methods used for sea target detection processing will be different, and sometimes a combination of several methods will achieve better enhancement effects [2]

Methods

Findings

Discussion

Conclusion