FFCA-YOLO for Small Object Detection in Remote Sensing Images

Issues such as insufficient feature representation and background confusion make detection tasks for small object in remote sensing arduous. Particularly when the algorithm will be deployed on board for real-time processing, which requires extensive optimization of accuracy and speed under limited computing resources. To tackle these problems, an efficient detector called FFCA-YOLO(Feature enhancement, Fusion and Context Aware YOLO) is proposed in this paper. FFCA-YOLO includes three innovative lightweight and plug-and-play modules: feature enhancement module(FEM), feature fusion module(FFM) and spatial context aware module(SCAM). These three modules improve the network capabilities of local area awareness,…

• NN
  National Natural Science Foundation of China

  Award: 61705104
• FR
  Fundamental Research Funds for the Central Universities

  Award: NJ2022025