Performance Analysis of Deep Learning YOLO models for South Asian Regional Vehicle Recognition

Abstract

For years, humans have pondered the possibility of combining human and machine intelligence. The purpose of this research is to recognize vehicles from media and while there are multiple models associated with this, models that can detect vehicles commonly used in developing countries like Bangladesh, India, etc. are scarce. Our focus was to assimilate the largest dataset of vehicles exclusive to South Asia in addition to the more common universal vehicles and apply it to track and recognize these vehicles, even in motion. To develop this, we increased the class variations and quantity of the data and used multiple variations of the YOLOv5 model. We trained different versions of the model with our dataset to properly measure the degree of accuracy between the models in detecting the more unique vehicles. If vehicle detection and tracking are adopted and implemented in live traffic camera feeds, the information can be used to create smart traffic systems that can regulate congestion and routing by identifying and separating fast and slow-moving vehicles on the road. The comparison between the three different YOLOv5 models led to an analysis that indicates that the large variant of the YOLOv5 architecture outperforms the rest.

Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers

“…Yolo has capabilities in fast detection [6,15] but has shortcomings in precision. Object detection algorithms have been widely applied in various fields including vehicle detection [16,17], objects [6,18], plant diseases [13,19,20], health [21,22], housing [23], road damage [24], natural disasters [25,26] to weapons detection [27]. Currently, Yolo is the most popular object detection method due to its accuracy and speed, but one of its shortcomings is the dataset used.…”