YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-theart object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14× compression rate of YOLOv4 with 49. 0 mAP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19. 1 FPS, and outperforms the original YOLOv4 by 5× speedup. Source code is at: https: //github. com/nightsnack/YOLObile.

Authors

• Yuxuan Cai
• Hongjia Li School of Medical Technology, Beijing Institute of Technology
• Geng Yuan University of Georgia
• Wei Niu University of Georgia
• Yanyu Li Northeastern University
• Xulong Tang
• Bin Ren College of William & Mary
• Yanzhi Wang Northeastern University

Keywords

No keywords are indexed for this paper.