计算机科学
目标检测
自动化
人工智能
深度学习
测距
建筑
鉴定(生物学)
任务(项目管理)
领域(数学)
智能交通系统
计算机视觉
机器学习
数据挖掘
实时计算
模式识别(心理学)
工程类
运输工程
地理
电信
数学
植物
机械工程
考古
系统工程
纯数学
生物
作者
Refaat Mohammad Alamgir,Ali Abir Shuvro,Mueeze Al Mushabbir,Mohammed Ashfaq Raiyan,Nusrat Jahan Rani,Md. Mushfiqur Rahman,Md. Hasanul Kabir,Shamsuddin Ahmed
标识
DOI:10.1109/iccit57492.2022.10055758
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI