Wind disturbances and body vibrations often cause image blurring in unmanned aerial vehicle (UAV) inspections, reducing target recognition accuracy. This study developed an enhanced UAV deblurring and target detection method based on an improved DeblurGANv2. A Haar wavelet-based image blur detection method was introduced, achieving high-precision identification of blurred regions through multi-level grayscale decomposition and edge feature extraction. An improved DeblurGANv2 network integrating an efficient channel attention module and modulated deformable convolution was designed to extract and restore blurred features effectively. Cross-channel feature correlation and an adaptive sampling strategy improved feature extraction and reconstruction accuracy. Experimental results show that the proposed method effectively restores edge structures and fine details in motion-blurred UAV images. Compared to DeblurGANv2, the restored images achieve a 37.6% increase in peak signal-to-noise ratio and an 11.4% improvement in structural similarity. For blurred image target detection, recognition accuracy improves by 18%, and recall increases by 30% over conventional methods.