A method and system for underwater image target detection

Abstract

The embodiment of the invention provides an underwater image target detection method and system, comprising the following steps: obtaining an underwater image to be detected, and utilizing MS-CNN denoise algorithm and dark channel prior denoising algorithm preprocess that underwater image to be detected to obtain a first image; inputting the first image into the trained preset feature pyramid network FPN, and outputting a target detection result; wherein the convolution layer of the preset FPN is a PVA network and a C. ReLU structure is added. As the underwater image to be detected is preprocessed, the improved FPN is used to detect the target from the preprocessed underwater image and output the target detection results without manual feature design. The detection process is time-consuming and the detection results have high accuracy. It can be well adapted to the practical application requirements such as automatic detection and automatic fishing.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and more specifically, to a method and system for detecting an underwater image target. Background technique [0002] As one of the important technologies for perceiving the underwater world, underwater machine vision has been widely used in ocean engineering, underwater exploration, target detection, biological monitoring, etc., providing rich resources for oceanographic and fishery scientific research. The information laid the foundation for the development of smart ocean and smart fishery. As a branch of underwater image target detection technology, it is mounted on underwater robots, applying image and video analysis, biomass detection, and biometric identification to the underwater environment, which promotes the development of ocean detection and fishery automation. [0003] However, the underwater environment usually has insufficient light, obvious noise, lo...

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Problems solved by technology

[0004] However, hand-designed feature engineering is time-consuming and laborious, and its robustness to complex underwater backgrounds is not good, and the recognition accuracy in complex underwater backgrounds is low. At the same time, existing target detection methods often have time-consuming problems when running. It cannot be well adapted to practical application requirements such as automatic detection and automatic fishing

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