Aiming at the few datasets and low detection rate when the traditional target detection algorithm applying to third-party construction target detection and illegally occupied building detection in the aerial images of drone,an aerial image target detection algorithm based on Aerial-YOLOv2 and transfer learning was proposed. Firstly,the trained network combining with data enhancement and transfer learning strategy was used to expand the dataset size,and K-means clustering analysis was used to obtain the number and size of anchor blocks that meet the characteristics of the proposed dataset. Secondly,the adaptive contrast enhancement was used to pre-process the image. Finally,the improved convolution module was proposed to replace the convolution block in YOLOv2,and the feature fusion multi-scale prediction method was combined for target detection. The comparison experiments of different algorithms and training strategies on the aerial images of drone were carried out. Results show that the accuracy and recall rate of the Aerial-YOLOv2 algorithm combined with various training strategies can respectively reach 95% and 91%,and the detection time per image is 14 ms. It can be seen that the algorithm is suitable for the intelligent detection of third-party construction targets and illegally occupied buildings in the aerial images of drone.