Ship target detection method based on joint training of deep learning features and visual features

Abstract

The invention provides a ship target detection method based on joint training of deep learning features and visual features, which includes the following steps: sample data collection, CNN feature extraction, traditional moment invariant feature and LOMO feature extraction, feature dimension reduction, feature fusion network FCNN construction, and training the network with sample data and testingthe model with test data. Compared with the prior art, the visual feature extraction process of the invention comprehensively considers the characteristics of the ship shape, color and texture, so that the detection process is interpretable, and other features other than the traditional features can be learned in the normalized CNN back propagation process. This method is fast and efficient, highaccuracy, for complex scenes such as clouds and fog, cloudy days, rain and other circumstances still have good detection results, high robustness. Features complementary to the traditional features can be extracted, and the speed is very fast, can achieve the effect of real-time monitoring.

Description

technical field [0001] The invention belongs to the field of computer vision for ship detection, and in particular relates to a ship target detection method for joint training of deep learning features and visual features. Background technique [0002] my country has a vast coastline, sea area and rich marine resources. With the continuous development of the economy, the number of ships at sea is increasing, and there is an urgent actual demand for ship inspection. Ship target detection is to use computer vision and image processing technology to detect ship targets of interest from images, and further extract a large amount of useful information. It has broad application prospects in both military and civilian fields. For example, in the civilian field, by obtaining information such as the position, size, driving direction, and driving speed of ships, specific sea areas and bay ports can be monitored, and marine water transportation, illegal fishing, illegal smuggling, ille...

AI Technical Summary

Problems solved by technology

However, the black box-style features are poorly understood, and ships of different sizes have different degrees of feature retention after convolution, which will also lead to inconsistencies in the detection results of different ships.

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