Semantic Segmentation of Panoramic Images for Real-Time Parking Slot Detection

Autonomous parking is an active field of automatic driving in both industry and academia. Parking slot detection (PSD) based on a panoramic image can effectively improve the perception of a parking space and the surrounding environment, which enhances the convenience and safety of parking. The challenge of PSD implementation is identifying the parking slot in real-time based on images obtained from the around view monitoring (AVM) system, while maintaining high recognition accuracy. This paper proposes a real-time parking slot detection (RPSD) network based on semantic segmentation, which implements real-time parking slot detection on the panoramic surround view (PSV) dataset and avoids the constraint conditions of parking slots. The structural advantages of the proposed network achieve real-time semantic segmentation while effectively improving the detection accuracy of the PSV dataset. The cascade structure reduces the operating parameters of the whole network, ensuring real-time performance, and the fusion of coarse and detailed features extracted from the upper and lower layers improves segmentation accuracy. The experimental results show that the final mIoU of this work is 67.97% and the speed is up to 32.69 fps, which achieves state-of-the-art performance with the PSV dataset.