Research on anti drone detection method based on super resolution reconstruction
Nowadays,drone technology is extensively utilized in civilian and military sectors.However,due to the ease of access and operation of drone technology,the problem of"black flight"and"indiscriminate flight"of drones is serious.Traditional anti drone detection methods often face issues such as false detections and missed detections in complex flight environments,resulting in inaccurate detection of drones.Therefore,a detection approach is introduced,which combines super-resolution reconstruction with the YOLOv5s algorithm.Firstly,an improved enhanced super-resolution reconstruction generative adversarial network(Real ESRGAN)is used to enhance image resolution,enabling the detection network to extract more feature information of small targets.Then an optimized YOLOv5s is used to detect the reconstructed image.Finally,the experimental results show that this method performs well in anti drone detection,with the accuracy up to 90.3%,which is better than classical object detection models such as SSD and YOLOv7.
super resolution reconstructionattention mechanismsmall object detectioncomplex backgroundanti drone algorithm
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