Rapid ship target detection method, storage medium and computing equipment

Abstract

The invention discloses a rapid ship target detection method, a storage medium and computing equipment, and the method comprises the steps: establishing a feature pyramid full-convolution network anda double-branch module which sequentially comprise an input layer, a feature extraction layer, a feature fusion layer and an output layer, determining a ship data set, inputting a generated training set into the built feature pyramid full-convolution network, and generating a target detection result. According to the method, the ship in the image can be rapidly and accurately detected; and the detection result is accurate and rapid, the requirement on embedded equipment is low, and the method has very high practical application value in various aspects such as military affairs, civil use and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a fast ship target detection method based on feature pyramid and region center correction, a storage medium and a computing device. Background technique [0002] With the rapid development of aerospace technology and sensor technology in various countries, the scale of remote sensing image data that humans can obtain continues to increase, and the image quality continues to increase. Compared with traditional low-resolution remote sensing images, high-resolution remote sensing images have a complex background layout and can express more fine information such as texture structure and spatial layout. Typical ground objects in high-resolution remote sensing images are aircraft, ships, and buildings. These objects have different appearances such as color and shape, and have the characteristics of large changes in direction and position. Target detection in high-...

AI Technical Summary

Problems solved by technology

[0003] Based on the dataset HRSC2016, target detection methods with high MAP for ships at this stage, such as R2CNN, RC1&RC2, RRPN, R2PN, RetinaNet-H, RRD, RetinaNet-R, RoI-Transformer, R3Det, ​​etc., are changing the input image. size, or to improve GPU performance, because the backbone network (such as Resnet101, Resnet50, etc.) used is too deep or the intermediate processing process is too cumbersome, the number of pictures processed per second, that is, the frame rate (FPS) is lower than 25

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