Open AccessSmart Vehicle Parking System Using Computer Vision and Internet of Things (IoT)
Author(s) -
Onate Egerton Taylor,
P. S. Ezekiel,
Victor T. Emmah
Publication year - 2021
Publication title -
european journal of information technologies and computer science
Language(s) - English
Resource type - Journals
ISSN - 2736-5492
DOI - 10.24018/compute.2021.1.2.12
Subject(s) - computer science , gridlock , cloud computing , the internet , internet of things , computer security , parking lot , haar like features , global positioning system , artificial intelligence , transport engineering , world wide web , telecommunications , feature extraction , engineering , face detection , operating system , civil engineering , facial recognition system , politics , political science , law
Internet of Things is the interconnectivity between things, individuals and cloud administrations by means of web, which empowers new plans of action. Because of these exchanges, immense volumes of information are smartly created and is shipped off cloud-based server through web; the information is being handled and broken down, bringing about significant and convenient activities for observing the car parking. The serious issue that is arising currently at a worldwide scale and developing dramatically is the gridlock issue brought about by vehicles. A worldwide scale and developing dramatically is the gridlock issue brought about by vehicles. Among that, finding a better parking sparking space in urban areas has become a major problem with an increase of the numbers of vehicles on a daily bases. Therefore making it difficult in having a better and safe parking spot. The system proposes an intelligent smart parking system using computer vision and internet of things. The proposed system starts by acquiring a dataset. The dataset is made up images of various vehicles, which was collected from the faculty of science car park at the Rivers State University, Port Harcourt, Rivers State Nigeria. We proposed two methods for vehicle/parking slot detection. The first method is the use of convolution neural network algorithm which is used with a haar cascade classifier in detection of multiple vehicles in a single picture and video, and put rectangular boxes on identified vehicles. This first method obtained an accuracy of 99.80%. In the second method, we made use of a Mask R-CNN, here we download a pre-trained model weights which was trained on a coco dataset to identify various objects in videos and pictures. The Mask R-CNN model was used to identify various vehicles by putting a bounding box on each of the vehicle detected, but one of the problem of the Mask R-CNN is that it quite slow in training, and it could not really detect all vehicles tested on a high quality high definition video. In summary our, trained model was able to detect vehicles and parking slot on high quality video and it consumes lesser graphic card.