The importance of obtaining details regarding transportation vehicles has been increasing in developing countries such as India. Object detection plays an important role in the automatic identification of the content of an image or video without human intervention. Various deep learning models have been developed for object detection using CNNs (convolution neural networks). This paper proposes a method for vehicle detection from still images using an optimized YOLO-NAS (You Only Look Once-Neural Architecture Search) frame work. This model is verified with earlier YOLO models for improved accuracy and optimization. The experiments were conducted on two publicly available datasets. Indian Driving Dataset (IDD) and DATS_2022 having exclusively images of various traffic scenes on Indian roads. The proposed method out performs the existing object detection models in terms of detection accuracy. Results show that the proposed method is good at detection accuracy measured in Average Precision and Recall.