Attention remote sensing image target detection method based on anchor frame

Abstract

An attention remote sensing image target detection method based on an anchor frame comprises the following steps: S100, embedding a jump connection feature pyramid module and a position attention module based on the anchor frame into a target detection model, and establishing an end-to-end single-stage remote sensing target detection model; S200, detecting a remote sensing target by adopting the end-to-end single-stage remote sensing target detection model; S300, outputting a detection result of the remote sensing target. The method can improve the precision and speed of the target detection model.

Description

technical field [0001] The disclosure belongs to the technical field of remote sensing image processing, and in particular relates to an anchor frame-based attentional remote sensing image target detection method. Background technique [0002] Remote sensing target detection is one of the most important tasks in the field of remote sensing images. With the rise of convolutional neural networks, remote sensing target detection has also made great progress. However, in object detection tasks, there are still some challenges that are difficult to solve, such as the detection of objects of different sizes and the detection of occluded objects. Due to the large difference in target size in remote sensing images, the uneven distribution of target objects, and the occlusion of trees and shrubs often further amplify the inherent problems in target detection tasks. [0003] For the detection of targets of different sizes, image pyramids can be used to solve the problem, but the comp...

AI Technical Summary

Problems solved by technology

In the attention module, the indiscriminate enhancement of the pixels in the bounding box will strengthen the inaccurate prediction results of the pixel area around the bounding box, which will interfere with the final prediction result.

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