人工标注无人机巡检航拍图像中销钉常见的缺陷耗时耗力，为此，文中采用深度学习算法 ＲetinaNet 来实现销钉缺陷的自动标注。考虑到如果直接对无人机现场采集到的不清晰图像进行检测，会出现识别精度偏低的问题，文中提出了一种基于生成对抗网络的缺陷智能识别方法，即通过生成器和鉴别器之间的互相博弈来增强局部纹理、边缘等细节信息，以改善此类图像质量，并结合缺陷智能识别算法提取准确的特征，以实现缺陷的智能识别。由于生成对抗网络训练用的模糊 － 清晰图像对难以获取，文中结合马尔可夫过程和子像素插值构建了模糊 － 清晰图像对。实验结果表明: ＲetinaNet 对清晰图像进行检测时，可以表现出优异的性能，而对于部分模糊图像，容易出现错标和漏标的情况; 文中构建的模糊 － 清晰图像对可以有效地训练生成对抗网络，使其具备去模糊功能，有利于卷积神经网络提取更加丰富的特征，进而提高模糊图像的识别率。

Manually marking the defect of the pin in the aerial survey of the drone is a time-consuming and labor- intensive task． To solve this problem，the deep learning algorithm ＲetinaNet was used to realize the automatic labe- ling of defects in pins． Given that if the unclear data collected by the drone is directly detected，the problem of low recognition accuracy can not be avoided，so a method for intelligent identification of defects based on generative ad- versarial network was proposed． That is to improve the local texture，edge and other details by the mutual game be- tween the generator and the discriminator，so as to improve the quality of such pictures and combine the defect in- telligent recognition algorithm model to extract accurate features to achieve defect detection． Finally，since the fuzzy- clear picture for generating network training is difficult to obtain，a fuzzy-clear picture pair was constructed by com- bining Markov process and sub-pixel interpolation． The experimental results show that the RetinaNet show excellent performance when detecting clear pictures，but for fuzzy picture，it can also lead to detection error．The constructed fuzzy-clear picture pair can effectively train the generated confrontation network to have deblurring function，which is beneficial to the convolutional neural network to extract more abundant features，thereby further improving the recognition rate of blurred pictures．