Aiming at the problems of large target size difference, blurred imaging and serious occlusion in sonar image, a target detection method based on corner is proposed. On the basis of CornerNet, an adaptive corner feature matching module is added to automatically select the features suitable for the target size and give them high weight, so as to realize the dynamic multi feature map hierarchical prediction and improve the quality of predicted corners; The prediction of embedded vector is cancelled, and a new layer structure is used to replace the embedded layer to realize corner grouping and reduce the false detection rate of the model. The experimental results show that the improved CornerNet has higher recall and better detection accuracy than other sonar image target detection algorithms.
KEYWORDS: Video, Video surveillance, Denoising, Cameras, Detection and tracking algorithms, Principal component analysis, Visual process modeling, Data modeling, Surveillance, Matrices
With the complexity of the video environment and the problem of possible noise during data transmission, traditional robust principal component analysis (RPCA) failed to obtain the lowest rank representation from corrupted data. A method of video denoising and an object detection algorithm based on the RPCA model with total variation and rank-1 constraint (TVR1-RPCA) is proposed; it employs the more refined prior representations for the static and dynamic components of the video sequences. The proposed method is based on RPCA under the framework of low-rank sparse decomposition; the rank-1 constraint is exploited to describe the strong low-rank property of the background layer, TV regularization is combined with l1 regularization to constrain the sparsity and spatial continuity of the foreground component, and l2 norm regularization is combined to constrain the noise to make up for the deficiencies of the existing RPCA model. In addition, an efficient algorithm based on the alternating direction method of multipliers is designed to solve the proposed video denoising and moving object detection issues. Our experiments on static and moving camera videos demonstrate that the proposed method is superior to the state-of-the-art methods in terms of denoising capability and detection accuracy.
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