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Radar target image detection method based on Precise ROI-Faster R-CNN

An image detection and radar target technology, which is applied in neural learning methods, radio wave measurement systems, instruments, etc., can solve the problems of high cost, poor universality, and complex technology of radar remote sensing images, so as to improve target detection and classification probability, The effect of reducing processing time and hardware cost

Active Publication Date: 2019-09-06
NAVAL AVIATION UNIV
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AI Technical Summary

Problems solved by technology

However, the cost of radar remote sensing image acquisition is high, the technology is complex, and the universality is poor. In comparison, radar has a wide range of applications, low cost, and excellent detection performance. It has broad application prospects in target detection.

Method used

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  • Radar target image detection method based on Precise ROI-Faster R-CNN

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Embodiment Construction

[0019] Such as figure 1 As shown, the processing flow of the present invention is divided into the following steps:

[0020] 1) Radar echo data collection, construct image training data set

[0021] Collect radar echo data under various observation conditions and areas to ensure the diversity of collected data samples, convert echo data information into image information, perform cropping and data enhancement processing on images, and then perform manual identification classification and label addition, Construct a complex and diverse radar imagery training dataset.

[0022] 2) Build a Precise ROI-Faster R-CNN target detection model

[0023] Such as figure 2 As shown, the structure of the Precise-Faster R-CNN target detection model constructed is divided into three parts: shared convolutional neural network, region proposal network (Region Proposal Network, RPN), classification and regression network, in which the shared convolutional neural network consists of ZFNet, VGG...

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Abstract

The present invention relates to a radar target image detection method based on Precise ROI-Faster R-CNN, and belongs to the technical field of radar signal processing. Firstly, the radar converts echo data information into an image and constructs a training data set; then, a Precise ROI-Faster R-CNN target detection model is established and comprises a shared convolutional neural network, a region suggestion network and a classification and regression network, and an ELU activation function, a Precise ROI Pooling method and a Soft-NMS method are adopted; inputting a training data set to carryout iterative optimization training on the model to obtain an optimal parameter of the model; and finally, inputting an image generated by real-time radar target echoes into the trained optimal target detection model for testing, and completing target detection and classification integrated processing. The method can intelligently learn and extract radar echo image features, is suitable for detection and classification of different types of targets in a complex environment, and reduces processing time and hardware cost.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing. More specifically, the invention relates to a radar target image detection method based on PreciseROI-Faster R-CNN, which can be used for intelligent processing of radar target detection. Background technique [0002] As the main means of target detection and surveillance, radar is widely used in the fields of public safety and national defense. However, affected by the complex ocean environment, low echo signal-to-clutter ratio, and complex motion characteristics of the target, the target echo is extremely weak and has low observability, which makes it difficult for the radar to detect targets in the clutter background to meet the actual needs. The detection technology of low-observable targets in clutter has become a key constraint factor and a worldwide problem, making it difficult to achieve robust, reliable and fast detection. [0003] In recent years, artificial intelligenc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G01S7/02G06N3/04G06N3/08
CPCG01S7/02G01S7/021G06N3/08G06V2201/07G06N3/045G06F2218/12Y02T10/40
Inventor 陈小龙牟效乾张林王国庆薛永华关键
Owner NAVAL AVIATION UNIV
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