End-to-end deep learning pipeline for scalable, deployable object detection engine in the traffic system

A wide variety of vehicles are moving on roads, and these vehicles are classified and detected by intelligent traffic systems using object detection. Various traditional methods are inefficient in producing good accuracy for detecting vehicles in traffic due to a lack of object discrimination capability. Object detection algorithms typically rely on deep convolutional neural networks, which require the host device with high computing capabilities, significantly limiting the applications of object detection algorithms for edge devices with limited computing capabilities. There is a need for an object detector to address the problem of detecting objects in traffic. This paper proposes a novel framework consisting of a deep learning model with the training-to-inference pipeline for object detection on images acquired from Indian city streets. The deep learning model has been used to streamline the conversion of the trained model to an optimized Triton-compatible (TensorRT) model. The Triton Inference Server model is evaluated on the Indian driving dataset (IDD) with NVIDIA Jetson AGX Xavier edge device. The model can easily deploy on traffic data collected from any client devices located remotely. The experimental results show that the Triton Inference outperformed the existing techniques. The proposed framework can be used to prevent traffic congestion. The proposed method achieved a precision of 93.8%, recall of 76.5%, and MAP of 80.5%, respectively, on IDD images with a resolution of 1920 x 1080.