Real-time vehicle detection and counting of multiple types is a difficult problem. To solve this problem, this study presents an efficient method based on single shot detection (SSD) to construct a vehicle detection and counting system. The proposed method named Fast-SSD first combines the Slim ResNet-34 with Single Shot MultiBox Detector. Then the authors limit the location prediction at each cell in the feature map and modify the detection network. When the input size of the picture is 300 × 300, Fast-SSD achieves the accuracy of 76.7 mAP on the PASCAL visual object classes 2007 test set. The network can be implemented at the speed of 20.8 FPS based on the GTX650Ti. Furthermore, they obtain the centre point of each type of vehicle which is detected by the Fast-SSD model in the image and set the virtual loop detectors to specify the detection range. The number of vehicles is calculated when the centre of the vehicle passes the virtual loop detector. Results show that the vehicle detection accuracy achieves 99.3% and the classification accuracy is 98.9%.
Fast single shot multibox detector and its application on vehicle counting system
IET Intelligent Transport Systems ; 12 , 10 ; 1406-1413
2018-10-15
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
object detection , image classification , fast single shot multibox detector , road vehicles , single shot detection , virtual loop detectors , Fast-SSD model , vehicle counting system , Slim ResNet-34 , visual databases , real-time vehicle detection , feature map and , feature extraction , GTX650Ti , detection range specification , PASCAL visual object classes 2007 test
Metadata by IET is licensed under CC BY 3.0
Fast single shot multibox detector and its application on vehicle counting system
Wiley | 2018
|Real-Time Train Detection Based on Improved Single Shot MultiBox Detector
British Library Conference Proceedings | 2020
|Real-Time Train Detection Based on Improved Single Shot MultiBox Detector
Springer Verlag | 2020
|