Performance and Memory Trade-offs of Deep Learning Object Detection in Fast Streaming High-Definition Images

Abstract

Deep learning models are associated with various deployment challenges. Inference of such models is typically very compute-intensive and memory-intensive. In this paper, we investigate the performance of deep learning models for a computer vision application used in the automotive manufacturing industry. This application has demanding requirements that are characteristic of Big Data systems, including high volume and high velocity. The application has to process a very large set of high-definition images in real-time with appropriate accuracy requirements using a deep learning-based object detection model. Meeting the run time, accuracy, and resource requirements require a careful consideration of the choice of model, model parameters, hardware, and environmental support. In this paper, we investigate the trade-offs of the most popular deep neural network-based object detection models on four hardware platforms. We report the trade-offs of resource consumption, run time, and accuracy for a realistic real-time application environment.