CN-YOLOv5-based multi-modal data fusion anti-unmanned aerial vehicle high-precision detection method

By adopting a multimodal data fusion detection method based on CN-YOLOv5 in the anti-UAV detection technology, the convolutional attention module and neural architecture search feature pyramid network is integrated, which solves the problem of low detection accuracy in the existing technology, and achieves high-precision and strong stability drone detection and tracking effects.

CN119716839APending Publication Date: 2025-03-28HARBIN INST OF TECH +1
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Patent Information

Application Number
CN202411838888.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-13
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The existing anti-UAV detection technology has problems such as low detection accuracy, high false alarm rate and missed alarm rate, especially in complex environments and multi-target scenarios.

Method used

Using a multimodal data fusion detection method based on CN-YOLOv5, an improved CN-YOLOv5 model is constructed by integrating the convolutional attention module (CBAM) and neural architecture search feature pyramid network (NAS-FPN), and a modified CN-YOLOv5 model is constructed, and multimodal data such as visual images, infrared images, radar data, radio frequency signals and audio signals are fused to perform in-depth inference and learning.

Benefits of technology

It significantly improves the accuracy of drone detection, effectively reduces the false alarm rate and missed alarm rate, can stably detect and track drones in complex environments, and has good environmental adaptability and robustness.

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Abstract

The invention relates to a multi-modal data fusion anti-unmanned aerial vehicle high-precision detection method based on CN-YOLOv5. The invention relates to the technical field of unmanned aerial vehicle detection, and the method comprises the steps: deploying a sensor, and carrying out the data collection; a CN-YOLOv5 model is built, and the model is trained; receiving data according to the trained model, judging whether the unmanned aerial vehicle exists or not, if yes, determining the type, position and speed information of the unmanned aerial vehicle, performing real-time tracking, and generating the flight path of the unmanned aerial vehicle. According to the invention, high-precision detection is realized. Through matching fusion of multi-modal data and a feature fusion method of an attention mechanism and optimization in the model, advantage complementation of the modal data is fully utilized, so that the model can more accurately extract and analyze the target features of the unmanned aerial vehicle, the detection accuracy is remarkably improved, the false alarm rate and the missing report rate are effectively reduced, and the detection efficiency is improved. And different types of unmanned aerial vehicles in different flight states can be accurately identified.
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