Two-stage remote sensing target detection method based on target center point estimation
A target detection and center point technology, applied in neural learning methods, computing, computer components, etc., can solve problems such as damage to the accuracy of target detectors and unconsidered location distribution characteristics of remote sensing images.
Active Publication Date: 2021-09-10
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AI-Extracted Technical Summary
Problems solved by technology
The invention relates to a two-stage remote sensing target detection method based on target center point estimation. A sample library is constructed by using a remote sensing image and an annotation file of an interested category target, a two-stage remote sensing target detection network based on target center point estimation is trained, and features of the interested category target on the remote sensing image are learned. And target detection is performed on a new remote sensing image by using the trained network model, thereby realizing automatic category judgment and bounding box positioning of an interested target on the remote sensing image. According to the method, the regional suggestion network is constructed according to the spatial position distribution characteristics of the targets on the remote sensing image, and multi-category target candidate frames are directly generated. Compared with a method in which dense target candidate frames are firstly generated, then the best candidate frame is selected from the dense target candidate frames by using a non-maximum suppression method, and subsequent target identification and bounding box correction are carried out, the method of the invention has higher efficiency and precision, and is more suitable for target detection tasks of remote sensing images.
Character and pattern recognitionNeural architectures +1
Computer visionNetwork model +2
- Experimental program(1)
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