A lightweight object detection framework for remote sensing images

Lang Huyan, Yun Peng Bai, Ying Li*, Dongmei Jiang, Yanning Zhang, Quan Zhou, Jiayuan Wei, Juanni Liu, Yi Zhang, Tao Cui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (SciVal)
49 Downloads (Pure)


Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.

Original languageEnglish
Article number683
Number of pages25
JournalRemote Sensing
Issue number4
Publication statusPublished - 13 Feb 2021
Externally publishedYes


  • Cost density
  • Deep learning
  • Feature fusion
  • Lightweight
  • Object detection
  • Remote sensing imagery


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