14. The method of claim 1 , wherein the selecting the at least one candidate contour region comprises: entering the extracted candidate contour regions to a trained first deep convolutional neural network (CNN) model; determining a confidence level of each extracted candidate contour region containing an annual inspection validity period; and choosing the extracted candidate contour region as a candidate contour region containing an annual inspection validity period when the confidence level of the extracted candidate contour region is greater than a predetermined threshold.
14. The method of claim 1 , wherein the selecting the at least one candidate contour region comprises: entering the extracted candidate contour regions to a trained first deep convolutional neural network (CNN) model; determining a confidence level of each extracted candidate contour region containing an annual inspection validity period; and choosing the extracted candidate contour region as a candidate contour region containing an annual inspection validity period when the confidence level of the extracted candidate contour region is greater than a predetermined threshold. 15. The method of claim 14 , wherein the first deep CNN model uses a 23-level model structure comprising 1 input layer, 21 convolutional layers and 2 classification layers, where the input layer is not counted into the total number of layers, and the first deep CNN model is trained by batches of contour regions with and without annual inspection validity periods.