Research On Motion Ship Video Detection Algorithm For Complex Scenes In Water

Fairway freight rates are cheap,and most of the bulk goods use waterway transportation.The intelligent videosurveillance system for ships in inland waterways is of great significance for transportation safety and improving transportation efficiency.In the actual scene,there are interferences such as reflection of the water surface and ripples of the ship.However,the traditional Gaussian background modeling and detection method based on the pixel difference between frames is inconsistent with its principle,and inevitably there are cases of misdetection and missed detection.For this reason,the paper combines the special projects of the shipbuilding network and carries out special research on this topic.This has important application value and theoretical significance.The paper describes the research status of ship detection at home and abroad,including shadow detection,moving target detection,etc.It points out the difficulties in ship detection.The mechanism of several commonly used shadow detection algorithms is analyzed,and their advantages and disadvantages are analyzed and compared.A random shadow partition algorithm based on hypergraph is proposed.The flaws in the false detection of Faster RCNN are analyzed.The details of the two process improvement of Faster RCNN are described.The ship detection algorithm with fusion sample sclection is given.The algorithm principle of the multi-layered HyperNet network is studied,and the position information and detection accuracy of the target ship are compared with the Faster RCNN,and the feasibility of the HyperNet network in practical application is verified.For the traditional shadow detection algorithm,the detection failure due to the similarity between the color of the ship's side and the color of the shadow.In this thesis,the algorithm principle of hypergraph segmentation is analyzed,the shadow space detection algorithm in color space is improved and optimized,and the random shadow detection partition algorithm based on hypergraph is givenFor the problem of false detection in the actual detection of the Faster RCNN algorithm,the paper proposes a ship detection algorithm that combines sample selection and rejection.The algorithm analyzes the principle of the CFAFT model.Firstly,the Fast RCNN classifier is used to filter the candidate regions generated by the RPN.Then in the classification stage,two binary classifiers were added on the basis of the original classifier for finer precision measurement.Experimental results show that the algorithm can solve the problem of misdetectionAiming at the accuracy of the target frame position information in the actual detection of the Faster RCNN,and the need to further improve the detection accuracy of the Faster RCNN algorithm,this paper presents a multi-featured ship detection algorithm.The algorithm gathers and integrates the characteristics of the high-resolution shallow layers,middle layers,and deep-seated feature maps that are rich in semantic information into a single space.With the actual traffic data set,it has good location information.Compared with Faster R-CNN,the average detection accuracy of mAP and accuracy were improved by 15%and 6.2%,respectivelyIn summary,the innovation and characteristics of the paper are? A hyper-graph-based random shadow detection partition algorithm was proposed to remove the shadow of the moving ship and improve the detection accuracy of the moving target? The two processes of moving target detection are optimized,and the ship detection algorithm with fusion sample selection is proposed.The false detection problem in the actual detection of the Faster RCNN algorithm is solved,and the precision of the moving target detection and the ship parameters of the inland waterway are improved? A multi-featured ship detection algorithm is proposed,which integrates shallow,intermediate,and high-level information,solves the accuracy problem of the target frame position information in the actual detection of the Faster RCNN,and improves the accuracy of target detection.