Due to the interference of complex background in UAV inspection image and the influence of external factors such as aerial shooting Angle, insulator object recognition will bring certain difficulty. When the commonly used Faster RCNN model is used for insulator object detection under complex background, there is the problem of missing detection of small object insulators that are distant or blocked. Therefore, this paper selects ResNet101 as the backbone network on the existing Faster RCNN model. The FPN structure is introduced to improve the detection accuracy of the occluded small object insulator, reduce the missed detection rate of occluded targets, and the channel attention mechanism SENet is added to enhance the insulator characteristics. The experimental results show that the improved model based on Faster RCNN achieves an accuracy of 932% in insulator object detection under complex background, which is 64% higher than that of the baseline model AP50, and is superior to some advanced object detection models at present, with high detection accuracy for insulators under complex background, and can solve the problem of false detection and missing detection of small object insulators.