  Vehicular object detection is the heart of any intelligent traffic system. It
is essential for urban traffic management. R-CNN, Fast R-CNN, Faster R-CNN and
YOLO were some of the earlier state-of-the-art models. Region based CNN methods
have the problem of higher inference time which makes it unrealistic to use the
model in real-time. YOLO on the other hand struggles to detect small objects
that appear in groups. In this paper, we propose a method that can locate and
classify vehicular objects from a given densely crowded image using YOLOv5. The
shortcoming of YOLO was solved my ensembling 4 different models. Our proposed
model performs well on images taken from both top view and side view of the
street in both day and night. The performance of our proposed model was
measured on Dhaka AI dataset which contains densely crowded vehicular images.
Our experiment shows that our model achieved<EMAIL_ADDRESS>of 0.458 with inference
time of 0.75 sec which outperforms other state-of-the-art models on
performance. Hence, the model can be implemented in the street for real-time
traffic detection which can be used for traffic control and data collection.
