Aiming at the low efficiency of manual disaster exploration, and the delay information feedback of back-end analysis using unmanned aerial vehicle in distribution network disaster exploration, an instantaneous detection model is proposed for the collapse of distribution network poles based on improved YOLO-ResNet hybrid neural network. Firstly, generalized intersection over union (GIoU) is introduced to improve the traditional YOLO-V3 algorithm to effectively enhance the accuracy of detecting the main body of poles. Then, ResNet-50 algorithm is used to locate endpoints and center line of poles, and a pole attitude judgment method is proposed to quickly calculate the pole tilt angle. Finally, a portable device for the model mentioned is developed, and the model and device are tested with field collected data. The results show that the overall accuracy of pole pose judgment based on the proposed model is 93.48%, the average power consumption of the portable device is 9 W, which is capable to intelligently analyze and summary the damaging status of the poles on the front end, and the effectiveness of the model and device is verified.