SRODNet: Object Detection Network Based on Super Resolution for Autonomous Vehicles

Yogendra Rao Musunuri, Oh Seol Kwon, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Object detection methods have been applied in several aerial and traffic surveillance applications. However, object detection accuracy decreases in low-resolution (LR) images owing to feature loss. To address this problem, we propose a single network, SRODNet, that incorporates both super-resolution (SR) and object detection (OD). First, a modified residual block (MRB) is proposed in the SR to recover the feature information of LR images, and this network was jointly optimized with YOLOv5 to benefit from hierarchical features for small object detection. Moreover, the proposed model focuses on minimizing the computational cost of network optimization. We evaluated the proposed model using standard datasets such as VEDAI-VISIBLE, VEDAI-IR, DOTA, and Korean highway traffic (KoHT), both quantitatively and qualitatively. The experimental results show that the proposed method improves the accuracy of vehicular detection better than other conventional methods.

Original languageEnglish (US)
Article number6270
JournalRemote Sensing
Issue number24
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • General Earth and Planetary Sciences


  • autonomous vehicles
  • modified residual block
  • object detection network
  • remote sensing data
  • super-resolution


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