ROV deformed target and small target recognition method based on convolution kernel screening SSD network

Abstract

The invention provides a target re-identification method based on hypersphere embedding of a densely connected convolution network. At first, that features of underwater deformation object in the video sequence are extracted according to the secret-level connected convolution network DenseNet, so that that gradient disappear is greatly reduced, feature propagation is enhanced, feature reuse and parameter learning processes are supported; from the viewpoint of fine-grained classification, from local integration to global integration, the characteristics of underwater deformation targets are extracted by means of grouping average pooling, the more accurate feature expression ability of underwater deformation target is obtained; focusing on the inter-class difference of underwater deformation individual targets by using hypersphere loss, i.e. angular triple loss, the difference in regional classification are classified to avoid the direct measurement of the Euclidean distance between theindividual target coding features of underwater deformation, a complete and continuous underwater deformed individual target re-recognition model based on multi-point placement is constructed. The invention finally completes the close supervision and process tracking of the underwater deformation target individual in the close-range multi-field observation.

Description

technical field [0001] The invention relates to a detection method based on underwater deformation targets and small targets, and belongs to the technical fields of intelligent information processing, target detection and underwater robots. Background technique [0002] The detection of underwater deformable targets and small targets is an indispensable link in most vision systems. In specific scene applications (such as video surveillance and other fields), automatic, fast, and highly robust target tracking has attracted attention. It has broad application prospects in video surveillance, traffic detection, intelligent robots, and submarine target detection and tracking. In addition, based on the strategic significance of the ocean, the ocean must be rationally developed, researched and utilized. [0003] Underwater robot (Remote Operated Vehicle, ROV) can replace human beings to work in complex and dangerous underwater environments due to its flexible maneuverability and ...

AI Technical Summary

Problems solved by technology

To this end, we have introduced an underwater robot control platform based on Raspberry Pi and flight control. For the current deep learning model, the deeper the model contains more parameters, which brings a significant increase in the amount of calculation. The large amount of calculation demand leads to deep learning. It cannot run in embedded devices in real time, and the increase in power consumption is also difficult to solve

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