To address the issue of complex non-uniform blurring in images captured by drones during high-speed flight and attitude jitter in dynamic road inspections, which severely degrades image quality and impacts subsequent object detection, this paper proposes an improved DeblurGAN-V2 image deblurring algorithm guided by inertial sensors. This algorithm utilizes camera inertial measurement unit (IMU) data as physical constraints, designs a gradient direction consistency loss function to enforce motion trajectory recovery, and employs dual path-aware loss to balance texture and structure. Additionally, an adaptive weighting discriminator is designed to evaluate the authenticity of generated images at different scales, thereby enhancing image quality. Experiments on a self-built drone road inspection dataset demonstrate that compared to the baseline DeblurGAN-v2 model, peak signal-to-noise ratio (PSNR） and structural similar ityindex measure (SSIM） improve by 1.52 dB and 0.056, respectively. Furthermore, in downstream vehicle object detection tasks, the false positive rate and false negative rate decrease by 9.2% and 18.1%, respectively.