Object re-identification method based on densely connected convolutional network hypersphere embedding

Abstract

The present invention provides a target re-identification method based on densely connected convolutional network hypersphere embedding. Firstly, the densely connected convolutional network DenseNet extracts the underwater deformation target features in the video sequence, which greatly reduces the disappearance of gradients, strengthens feature propagation, and supports The process of feature reuse and parameter learning, and then from the perspective of fine-grained classification, from the local integration to the global, using the group average pooling idea to refine and extract the features of underwater deformation targets at all levels, to obtain more accurate underwater deformation target feature expression capabilities, and to The hypersphere loss, that is, the angular triple loss, focuses on the inter-class differences of underwater deformation individual targets, distinguishes intra-class differences, avoids directly measuring the Euclidean distance between the encoding features of underwater deformation individual targets, and constructs a complete underwater vision system with multi-point deployment. A continuous underwater deformation individual target re-identification model. The present invention finally completes the close supervision and process tracking of the underwater deformation target individual in the short-distance multi-field observation.

Description

technical field [0001] The invention relates to an underwater deformation and moving target re-identification method based on densely connected convolutional network hypersphere embedding, and belongs to the technical fields of intelligent information processing and target detection and recognition. Background technique [0002] my country has a vast sea area and rich fishery resources, so marine monitoring is very necessary. Not only can it detect abnormal underwater targets in time and take countermeasures, but it can also improve the marine environment in time to avoid pollution to the marine environment and damage to the ecological environment. Therefore, it is of great significance to carry out long-term and effective multi-camera and multi-angle monitoring of the main resources in the marine environment, and also provides a basis for aquaculture fisheries and marine fishing industries. The behavioral analysis of underwater deformable targets provides data and informati...

AI Technical Summary

Problems solved by technology

That is, the main problems existing in the prior art: (1) Due to the complex optical imaging conditions of the ocean, factors such as post-scattering of underwater imaging, turbid water, sediment, ocean currents, phytoplankton, and swinging aquatic plants will cause optical image quality to decline or The introduction of interference will cause errors in target re-identification; (2) The rapid swimming and large-scale shape changes of underwater deformable targets will also cause difficulties in the monitoring of underwater deformable targets

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