Research On Perception Of Underwater Monocular Vision Based On Machine Learning

With the increase of human demand for marine resources,the exploration and development of the oceans are also deepening,and underwater vehicles and related technologies are becoming hot spots in scientific research.Vision is a very important part of underwater vehicle technology and the most intuitive observation technology.It can effectively detect and recognize underwater targets.It is widely used in autonomous navigation of underwater vehicles,seabed detection and underwater equipment maintenance and other related fields.Therefore,the research of underwater positioning and recognition technology based on vision has important scientific research significance and significant social reality value.In this paper,the monocular vision system is studied to realize the recognition and tracking of underwater targets,construct spatial distribution information,and provide more sensing information for underwater vehicle operation.Specific research contents are as follows:Firstly,the camera is modeled and analyzed,and the parameters of the camera are calculated and the underwater image is corrected by means of underwater calibration.Then,the underwater image enhancement method is systematically studied.Aiming at the problem of damage to image information caused by underwater light scattering absorption,an improved Retinex enhancement method based on statistical learning is proposed,and relevant experiments are carried out to verify the effectiveness of the improved image enhancement method.Secondly,the method of underwater three-dimensional reconstruction based on sequence frames is systematically studied.The spatial distribution of the environment is reconstructed by establishing the position and attitude relationship between frames according to the motion law of the feature points in the inter-frame images.In order to improve the number and uniformity of feature points,an improved SURF algorithm based on gradient amplitude pre-operation is proposed in this paper.Through experiments and simulation analysis,this method has better noise suppression ability than traditional feature extraction methods,and effectively improves the accuracy and quality of three-dimensional reconstruction.Thirdly,target recognition algorithm is studied,underwater target recognition is carried out by depth learning method,and underwater fish and other targets are detected by feature extraction,multi-scale fusion,maximum suppression regression method.Experiments on fish and other underwater targets were carried out in different environments.Finally,the method of particle filter is studied to track underwater targets.The principle of particle filter is introduced.The particle convergence effect is improved by improving the attraction strategy of particle swarm optimization.The CEI evaluation index is introduced to analyze the advantages and disadvantages of the algorithm.The feasibility of the algorithm is verified by the comparison experiments on land and underwater.