JUVDsi v1: developing and benchmarking a new still image database in Indian scenario for automatic vehicle detection.

Designing an automatic vehicle detection (AVD) system from still images or videos would be a useful tool to cater to the requirements of the traffic management system. Over the past few years, numerous databases have been developed for the use of researchers in this field of AVD. However, most of them are not acceptable in the Indian scenarios due to certain practical constraints like the road infrastructure, nature of congestion, and vehicle types commonly found in India. The aim of this research is to develop a still image database, named as JUVDsi v1, which includes nine different types of vehicle classes collected through mobile phone cameras in various ways for designing an automated traffic management system. Identifying and analyzing the shortcomings of existing databases, the developed database presents an improvement to address such bottle-necks. Furthermore, the efficiency of this database is evaluated using an ensemble of three state-of-the-art deep learning architectures. At first, each vehicle in the scene images is localized and categorized. Five base object detection models, namely, YOLOv3, Faster-RCNN, RFCN, SSDv1 and SSDLitev2 are used. Finally, the Weighted Boxes Fusion technique is used as the ensemble method (ensemble of best three out of the five base learners), thereby enhancing the performance obtained by the individual object detection models. The database can be found at: https://github.com/JUVDsi/JUVD-Still-Image-database.git. [ABSTRACT FROM AUTHOR]