Single-Fusion Detector: Towards Faster Multi-Scale Object Detection

Arren Matthew C. Antioquia, Daniel Stanley Tan, Arnulfo Azcarraga, Kai-Lung Hua

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review


Despite recent improvements, the arbitrary sizes of objects still impede the predictive ability of object detectors. Recent solutions combine feature maps of different receptive fields to detect multi-scale objects. However, these methods have large computational costs resulting to slower inference time, which is not practical for real-time applications. Contrarily, fusion methods depending on large networks with many skip connections demand larger memory requirement, prohibiting usage in devices with limited memory. In this paper, we propose a more computationally efficient fusion method which integrates higher-order information to low-level feature maps using a single operation. Our method can flexibly adapt to any base network, allowing tailored performance for different computational requirements. Our approach achieves 81.7% mAP at 41 FPS on the PASCAL VOC dataset using ResNet-50 as the base network, which is superior in terms of both speed and mAP as compared to several state-of-the-art baselines, even those which use larger base networks.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
Subtitle of host publicationProceedings
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
Publication statusPublished - Sept 2019
Externally publishedYes
EventIEEE International Conference on Image Processing - Taipei, Taiwan, Province of China
Duration: 22 Sept 201925 Sept 2019


ConferenceIEEE International Conference on Image Processing
Abbreviated titleICIP 2019
Country/TerritoryTaiwan, Province of China
Internet address


  • Convolutional Neural Networks
  • Deep Learning
  • Feature Fusion
  • Object Detection
  • Object Recognition


Dive into the research topics of 'Single-Fusion Detector: Towards Faster Multi-Scale Object Detection'. Together they form a unique fingerprint.

Cite this