Enhancing the Performance of Deep Learning Model Based Object Detection using Parallel Processing (Work In Progress Paper)
Omar Imran, Shikharesh Majumdar, Sreeraman Rajan · 2024
The need for accelerated object detection is paramount for safety critical applications such as autonomous vehicles. This paper focuses on leveraging parallel processing techniques for enhancing the performance of object detection. Specifically, this research engineers system performance by timely detection of common objects encountered by vehicles, such as other automobiles, pedestrians, and bicycles. Deploying popular pretrained deep learning models like the You Only Look Once (YOLO) model within the Apache Spark framework, the potential enhancements in detection speed achieved through parallel processing are investigated. The capability of the system to efficiently handle large datasets and distribute time-critical applications across multiple nodes is explored to improve both latency and scalability. The one-factor-at-a-time method is used to assess the impact of different system and workload parameters on performance. Of particular interest is the impact of Spark data partitioning on performance, especially for driving scenarios where the number of objects are changing rapidly. A novel data partitioning technique that uses the principles of entropy is utilized. The overall performance objective of this research will be to improve speed for object detection in cars which can improve safety in time critical events such as sudden braking or turning.