UAV High-Speed Target Reconnaissance and Deblurring

In military operations, the detection of enemy targets by reconnaissance aircraft usually results in obvious motion blur in imaging due to the rapid movement or position change of the target, which poses a challenge for accurate target identification. To address the imaging blur issue in high-speed dynamic scenarios and enhance the visual quality of enemy target imagery, this study proposes a blurred image restoration method based on reconnaissance imaging models. First, there is a lack of reliable simulation models and quantitative frameworks to assess the extent of such blurring. To this end, we developed an optical imaging simulation model for imaging systems that incorporates motion blur effects to more accurately reflect real-world scenarios. To fulfill the need for quantitative metrics, we introduce a velocity estimation method that calculates the blur length and accurately measures the degree of blur removal to estimate the target motion velocity to validate the real-world imaging of a reconnaissance aircraft imaging system. Additionally, to address the problem of imaging blurring, we propose a motion deblurring optimization strategy based on the pre-trained DeblurGAN-v2 model, which is specifically designed to restore the clarity of target imaging under high-speed dynamic conditions. The effectiveness of the simulation model and the deblurring strategy is rigorously verified through a series of low-speed UAV tests simulating battlefield conditions. The results show that our equivalent imaging model is able to achieve minimal relative error and accurately reproduce motion-induced blur when detecting a variety of ground targets.