| dc.description.abstract | This thesis presents a vehicle detection system with deep learning. We use two detectors based on deep learning, vehicle type detector and plate number detector. The former is customized for model and color classification, and the latter is for License Plate Recognition (LPR). The vehicle type detector is able to predict 100 models and 11 colors in Taiwan, and it takes a whole image as input without cropping car regions, which considerably different from most of the current vehicle type classification methods using cropped car regions as input. In addition, traditional approaches to solve LPR problem typically are broken down into the localization, segmentation, and recognition steps. Rather than doing those preprocess steps, the plate number detector we proposed can operate directly on plate images with high performance in angularly skewed, various light, and low resolution condition. Considering the need for adding new classes for vehicle type detector in the future, we design an auto-labeling flow to automatically create bounding box labels for training. After getting the information of color, model, and plate number, we can search the plate number in the database of registered vehicle to confirm whether information is consistent. In this thesis, we develop two user interfaces (UI) for mobile device and street monitoring respectively. The user can know whether the car is stolen vehicle immediately by photographing it with smartphone camera. Additionally, our system can also achieve real-time video analysis for street monitoring. Notably, from the experimental results, our method is allowed to simultaneously detect all vehicles at one frame, even in skew angle. | en_US |