| dc.description.abstract | Modern intelligent transportation systems
heavily rely on vehicle type classification technology. Deep
learning-based vehicle type classification technology has
sparked growing concern as Image Processing, Pattern
recognition, and Deep Learning have all advanced.
Convolutional neural work, particularly You Only Look
Once (YOLO), has demonstrated significant benefits in
image classification and object detection during the past
few years. Due to its ability to forecast objects in real-time,
this algorithm increases detection speed. High accuracy:
The YOLO prediction method yields precise results with
few background mistakes. Additionally, YOLO is aware of
generalized object representation. This method, which
ranks among the best for object detection, performs
significantly better than R-CNN techniques. In this paper,
YOLOv5 is used to demonstrate vehicle type detection;
YOLOv5 m model was chosen since it suits mobile
deployments, The model was trained with a dataset of
9200 images, where 2300 images were allocated for
each class with a variety of vehicles. Experimental
results for 100 epochs with a batch size of 16 show
mAP@.5 at 78.1% and mAP@.5:.95 at 71.7% trained and
tested on four vehicle classes. | en_US |
