Single-stage fine-grained target detection method and device based on remote sensing images

By constructing a single-stage fine-grained target detection method for remote sensing images, and utilizing convolutional neural networks and positive/negative sample allocation algorithms, the accuracy and real-time performance issues of fine-grained target detection in remote sensing images are solved, achieving efficient target recognition and localization.

CN119992334BActive Publication Date: 2026-05-26TIANJIN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TIANJIN UNIV
Filing Date
2025-01-24
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing fine-grained target detection technologies for remote sensing images are insufficient in terms of recognition accuracy and real-time performance. They are difficult to accurately distinguish fine-grained targets of multiple scales and angles in complex backgrounds, and existing models have high computational complexity, making it difficult to meet real-time requirements.

Method used

A single-stage fine-grained target detection method based on remote sensing images is adopted. Training and test sets are constructed by acquiring image data and label data. A feature extraction network is constructed using a convolutional neural network. Feature extraction is performed by combining a backbone network and a feature pyramid. The sample learning is optimized by a positive and negative sample allocation algorithm. The model is trained using the PyTorch deep learning framework. Finally, the target's category and location information are output.

Benefits of technology

It improves the accuracy and precision of fine-grained target detection, reduces category detection errors, has a simple algorithm process and good real-time performance, and is adaptable to target detection at different resolutions.

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Abstract

This invention relates to a single-stage fine-grained target detection method and apparatus based on remote sensing images. The method includes: acquiring image data and label data; cropping and flipping the image data to construct training and testing sets; constructing a feature extraction network using a convolutional neural network as the detection model; extracting features from the image data using the feature extraction network; and outputting the location and category information of fine-grained targets in the image. A positive and negative sample allocation algorithm is used to divide the location information boxes into positive and negative samples, and the convolutional neural network is used to learn the allocation of positive and negative samples. Based on the PyTorch deep learning framework, the network structure is written in Python, and the convolutional neural network is trained using the image and label data in the training set to obtain the target detection model. The image data to be detected is used as input to the target detection model to output the category and location information of the target objects in the image data to be detected.
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