Parking-Lot Vehicles Detection from a Low-Angle Camera Perspective Based on Improved Mask R-CNN

Abstract

Camera-based parking occupancy detection driven by deep learning algorithms is a promising technique for building the parking guidance and information system. However, when the available camera is looking at a long parking lot with a relatively low angle, the deep learning method will fail to detect vehicles accurately as vehicles closer to the camera will block those further away. In this study, we provide an improved Mask R-CNN algorithm which is also effective in detecting vehicles for a low-angle camera perspective. Firstly, we introduce the Selective Kernel Networks (SKNet) in the backbone architectures. Secondly, we build a path with clean lateral connections from the low level to the top ones at the back of Feature Pyramid Networks (FPN). Thirdly, we replace the Non-Maximum Suppression (NMS) with the Soft-NMS. Compared to the original Mask R-CNN, the improved ones have better performance, particularly for a low-angle camera perspective.