Trucks cannot be accurately identified when they do not follow the prescribed time and route on urban roads by avoiding cameras and other means. In view of this, an urban road truck detection method based on improved Faster RCNN is proposed. Features are extracted by performing convolution and pooling operations on the vehicle images passed into the backbone network. The feature pyramid network (FPN) is added to improve the accuracy of multi-scale target detection. At the same time, the K-means clustering algorithm is applied to the dataset to obtain new anchor boxes. Region proposal network (RPN) is utilized to generate proposal boxes and complete-IoU (CIoU) loss function is used for replacing the smoothL1 loss function of the original algorithm to improve the accuracy of vehicle detection. The experimental results show that the improved Faster RCNN increases the average precision (AP) for truck detection by 7.2% and the recall by 6.1%. The improved method reduces the possibility of missed detection and has a good detection effect in different scenarios.