PAINet: Toward Fast and Efficient Parking Lot Lane Detection

Abstract

Parking lot lane detection is a critical component of automated driving technology, requiring high accuracy, speed, and ease of deployment. In this study, we aimed to develop a comprehensive dataset of parking lane lines and construct an innovative parking lot recognition model. The model, named the Point Angle Instance Network (PAINet), effectively clusters each key point by embedding angle information, thus enhancing the stability and training efficiency. Additionally, we have developed an information collection module to address the issue of fault detection of the guidance arrow in parking lot environments. The performance of the model was tested and evaluated using the created parking lane dataset, and promising results were obtained. The model achieved an F1 score of 85.48%, an FPS of 164, and a GFLOPs of 4.1 in the task of parking lot lane detection in a surround-view situation. These results indicate the accuracy, practicality, and real-time performance of the model, highlighting its potential for use in automated driving systems.

Keywords

• Autonomous vehicles
• lane detection
• computer vision
• neural networks