Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh

The task of locating and classifying different types of vehicles has become a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification and many more. In recent times, Deep Learning models have been dominating the field of vehicle detection. Yet, Bangladeshi vehicle detection has remained a relatively unexplored area. One of the main goals of vehicle detection is its real-time application, where `You Only Look Once' (YOLO) models have proven to be the most effective architecture. In this work, intending to find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh, we have conducted a performance analysis of different variants of the YOLO-based architectures such as YOLOV3, YOLOV5s, and YOLOV5x. The models were trained on a dataset containing 7390 images belonging to 21 types of vehicles comprising samples from the DhakaAI dataset, the Poribohon-BD dataset, and our self-collected images. After thorough quantitative and qualitative analysis, we found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.Comment: Accepted in 25th ICCIT (6 pages, 5 figures, 1 table

Substantial and sustained reduction in under-5 mortality, diarrhea, and pneumonia in Oshikhandass, Pakistan : Evidence from two longitudinal cohort studies 15 years apart

Funding Information: Study 1 was funded through the Applied Diarrheal Disease Research Program at Harvard Institute for International Development with a grant from USAID (Project 936–5952, Cooperative Agreement # DPE-5952-A-00-5073-00), and the Aga Khan Health Service, Northern Areas and Chitral, Pakistan. Study 2 was funded by the Pakistan US S&T Cooperative Agreement between the Pakistan Higher Education Commission (HEC) (No.4–421/PAK-US/HEC/2010/955, grant to the Karakoram International University) and US National Academies of Science (Grant Number PGA-P211012 from NAS to the Fogarty International Center). The funding bodies had no role in the design of the study, data collection, analysis, interpretation, or writing of the manuscript. Publisher Copyright: © 2020 The Author(s).Peer reviewedPublisher PD