Efficient street parking sign detection and recognition using artificial intelligence

Traffic congestion in urban centers presents a pressing challenge for mobility and quality of life. Addressing this issue requires innovative solutions, with a key focus on leveraging the capabilities of autonomous and human-driven vehicles. A crucial aspect of this effort involves integrating parking sign detection technology to alleviate congestion. Despite its promising potential to improve environmental conditions and productivity, the domain of parking sign detection faces substantial challenges stemming from the diversity of sign types, complex detection requirements, and environmental variables. This thesis introduces an innovative approach to the precise detection and recognition of street parking signs, with the aim of integration into vehicle systems. Using our unique and extensive dataset, we conducted a comparative analysis of various object detection networks, aiming to select a model that balances computational efficiency and performance accuracy. Our evaluations revealed the superior performance of the You Only Look Once (YOLO) object detection models, particularly YOLOv7-X at the time, in terms of accuracy and computational complexity. Initially, the YOLOv7-X deep learning network is employed for the detection of parking signs in a dataset comprising videos captured by car cameras in Vancouver. Subsequently, a matching network utilizing the Triplet Loss function is applied for precise identification, while we leverage temporal information to further enhance the accuracy of detection and recognition. Performance evaluation demonstrated the robustness of our approach, yielding a mean Average Precision (mAP) of 97.4% for parking sign detection and a remarkable 91% accuracy in parking sign identification in a dataset of 43 different classes.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat