Aiming at the frequent diseases of tunnel portal built in the harsh environment of plateau region and the problems of low efficiency and high risk of traditional manual disease detection method, a disease detection method of tunnel portal in plateau region based on UAV image and deep learning was proposed. Firstly, an Unmanned Aerial Vehicle (UAV) was used to collect the disease images of a tunnel portal in the plateau region of Xinjiang, and a multi disease semantic segmentation dataset was constructed. Then, based on DeeplabV3+, an improved model TP-DeeplabV3+ was proposed, which used MobileNetV2 as the backbone feature extraction network to reduce the model parameters; Focalloss was used as the loss function to solve the problem of category imbalance in disease images; CA attention mechanism was uesd to improve the segmentation performance; and the disease quantification method was proposed. Experiment results show that TP-DeeplabV3+ can reach 88.37% and 94.93% of mIoU and mPA on the testset, and the model volume is reduced by 88.83%; The absolute error of the proposed disease quantification method for disease coverage rate is less than 0.3%, and the relative error is maintained below 7.31%. Compared with the traditional manual method, the proposed method can realize the intelligent detection of tunnel portal safely and accurately in plateau region.