Open AccessVehicle and Parking Space Detection Based on Improved YOLO Network Model
Author(s) -
Xin Ding,
Ruidi Yang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012084
Subject(s) - object detection , computer science , artificial intelligence , parking lot , feature (linguistics) , computer vision , residual , object (grammar) , space (punctuation) , parking space , real time computing , pattern recognition (psychology) , engineering , transport engineering , algorithm , linguistics , philosophy , civil engineering , operating system
YOLO has a fast detection speed and is suitable for object detection in real-time environment. This paper is based on YOLO v3 network and applied to parking spaces and vehicle detection in parking lots. Based on YOLO v3, this paper adds a residual structure to extract deep vehicle parking space features, and uses four different scale feature maps for object detection, so that deep networks can extract more fine-grained features. Experiment results show that this method can improve the detection accuracy of vehicle and parking space, while reducing the missed detection rate.