Hu, JuhuaJin, Yin2022-04-192022-04-192022-04-192022Jin_washington_0250O_23911.pdfhttp://hdl.handle.net/1773/48418Thesis (Master's)--University of Washington, 2022Correctly interpreting the complex parking sign and finding a suitable parking spot is always a challenging task to do in a short time. An automatic parking sign understanding assistance can improve the efficiency of a driver’s daily life. Besides, as Tesla becomes popular, automatic driving is also becoming a prevalent task. However, the current studies on self-driving steering command mainly focus on the driving part. The smart parking task is still an open task. In this thesis, we aim to handle a subtest of the automatic parking sign understanding problem, that is, real-time parking sign detection, to provide real-time parking sign localization for further sign interpretation. Specifically, with a real-time video streaming of the street view as input, we aim to detect parking signs on the street that will inform the parking rules over the current road. In this thesis, we achieved two main goals: 1) generating a diverse parking sign dataset with a size over 4,000 that covers complex street view; 2) training a well-performance real-time parking sign detection model with Yolov5. Our parking sign detection model achieves a mean average precision at Intersection over Union threshold 50% (mAP@.5) of 0.968 and an inference speed of 6.1ms per image that meets the real-time detection requirement. Moreover, the model size is only 14.4 MB which is small enough to fit in small and less powerful devices like mobile phones or autonomous cars.application/pdfen-USCC BY-NC-NDParking Sign Detectionreal-time DetectionYolov5Computer scienceComputer science and systemsThesis