Ship multi-target detection method based on improved anchor point box generation model

Abstract

The invention provides a ship multi-target detection method based on an improved anchor point box generation model. The ship multi-target detection method comprises the steps of obtaining an SAR shipimage; constructing a low-complexity network architecture, and putting the image into a low-complexity network to generate a feature mapping space; generating an initial anchor point box by adopting aclustering method based on shape similarity; and on the basis of the generated initial anchor point box, generating a new candidate box in the low-complexity feature space by adopting a sliding window mechanism, and performing regression training on the candidate box for ship multi-target detection. The method solves the problems of low algorithm efficiency and detection quality caused by complexnetwork and poor candidate box quality, and has good accuracy. Due to the fact that the low-complexity network architecture is adopted for detection, from the perspective of statistical analysis, thelarger the data acquisition amount is, the more the detection times are, and the better the detection effect is.

Description

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AI Technical Summary

Benefits of technology

This technology helps solve issues with complicated networks and poor image resolution due to complexity or lacking good frames during sea imagery scans. It also enhances the ability to detect multiple targets simultaneously without overlapping them on top of each other's background noise levels. By utilizing this methodology, it can improve both speed at which ships are detected (the time required) and reduce false alarms while still maintaining high sensitivity when looking through large amounts of data captured by different cameras.

Problems solved by technology

In this patented problem addressed in the technical solution described in the patents, the objective of these methods is how accurately detect both smaller and larger sized sea shipping vessels while maintaining their overall shape or size consistency across multiple imagery datasets captured from diverse sources like airborne radar systems. Current detection techniques have limitations due to factors including complexity structure, poor performance, excessive learning data collection requirements, and difficulty in adjustment during deployment.

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