Presentation
Neural Network Optimization and Performance Analysis for Real-Time Object Detection at the Edge
DescriptionReal-time object detection is an important and computationally intensive task that is gaining more attention in the field of autonomous systems. Recently, a novel object detection algorithm called RT-DETR has emerged, demonstrating superior speed compared to the popular YOLO series. In recent years, many edge devices optimized for artificial intelligence have been developed, allowing for faster model inference. Our study uses NVIDIA TensorRT to optimize models in the task of object tracking and detection on NVIDIA’s Orin device. Our best resulting model is the FP16 model with DLA with an average inference time of 19.9416 milliseconds and throughput of 50.1465 (frames) per second. This is a five-fold improvement compared with the standard unoptimized Pytorch FP32 model, with practically no accuracy sacrifice. Our study shows that applying TensorRT and quantization on object tracking and detection on NVIDIA’s Orin device is effective in reducing prediction time, allowing for faster detection.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX