Domain-Specific Approximation for Object Detection

Ting Wu Chin, Chia Lin Yu, Matthew Halpern, Hasan Genc, Shiao-Li Tsao, Vijay Janapa Reddi

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles. To enable such innovative systems, we need faster object detection. In this work, we investigate the trade-off between accuracy and speed with domain-specific approximations, i.e. category-aware image size scaling and proposals scaling, for two state-of-the-art deep learning-based object detection meta-architectures. We study the effectiveness of applying approximation both statically and dynamically to understand the potential and the applicability of them. By conducting experiments on the ImageNet VID dataset, we show that domain-specific approximation has great potential to improve the speed of the system without deteriorating the accuracy of object detectors, i.e. up to 7.5x speedup for dynamic domain-specific approximation. To this end, we present our insights toward harvesting domain-specific approximation as well as devise a proof-of-concept runtime, AutoFocus, that exploits dynamic domain-specific approximation.

Original languageEnglish
Pages (from-to)31-40
Number of pages10
JournalIEEE Micro
Issue number1
StatePublished - 1 Jan 2018


  • approximate computing
  • autonomous systems
  • object detection

Fingerprint Dive into the research topics of 'Domain-Specific Approximation for Object Detection'. Together they form a unique fingerprint.

Cite this