Open AccessDeep learning‐based vehicle occupancy detection in an open parking lot using thermal camera
Author(s) -
Paidi Vijay,
Fleyeh Hasan,
Nyberg Roger G.
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0468
Subject(s) - occupancy , object detection , deep learning , artificial intelligence , computer science , computer vision , parking lot , detector , real time computing , machine learning , engineering , pattern recognition (psychology) , civil engineering , telecommunications
Parking has been a common problem over several years in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, the aim of the study is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect videos during varying environmental conditions and frames from these videos were extracted to prepare the dataset. The frames in the dataset were manually labelled as there were no pre‐labelled thermal images available. Vehicle detection with deep learning algorithms was implemented to perform multi‐object detection. Multiple deep learning networks such as Yolo, Yolo‐conv, GoogleNet, ReNet18 and ResNet50 with varying layers and architectures were evaluated on vehicle detection. ResNet18 performed better than other detectors which had an average precision of 96.16 and log‐average miss rate of 19.40. The detected results were compared with a template of parking spaces to identify vehicle occupancy information. Yolo, Yolo‐conv, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time and are suitable for real‐time detection while Resnet50 was computationally expensive.