Small Object Detection in Unmanned Aerial Vehicle Images Using Feature Fusion and Scaling-Based Single Shot Detector With Spatial Context Analysis

• Published in: IEEE transactions on circuits and systems for video technology Vol. 30; no. 6; pp. 1758 - 1770
• Main Authors: Liang, Xi, Zhang, Jing, Zhuo, Li, Li, Yuzhao, Tian, Qi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Context; Datasets; Deconvolution; Detectors; Feature extraction; feature fusion; feature scaling; Image detection; Image enhancement; Modules; Object detection; Object recognition; Photography; Pyramids; Remote sensing; Scaling; Sensors; single shot detector; small object detection; spatial context analysis; Unmanned aerial vehicle (UAV) image; Unmanned aerial vehicles
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2019.2905881

Objects in unmanned aerial vehicle (UAV) images are generally small due to the high-photography altitude. Although many efforts have been made in object detection, how to accurately and quickly detect small objects is still one of the remaining open challenges. In this paper, we propose a feature fusion and scaling-based single shot detector (FS-SSD) for small object detection in the UAV images. The FS-SSD is an enhancement based on FSSD, a variety of the original single shot multibox detector (SSD). We add an extra scaling branch of the deconvolution module with an average pooling operation to form a feature pyramid. The original feature fusion branch is adjusted to be better suited to the small object detection task. The two feature pyramids generated by the deconvolution module and feature fusion module are utilized to make predictions together. In addition to the deep features learned by the FS-SSD, to further improve the detection accuracy, spatial context analysis is proposed to incorporate the object spatial relationships into object redetection. The interclass and intraclass distances between different object instances are computed as a spatial context, which proves effective for multiclass small object detection. Six experiments are conducted on the PASCAL VOC dataset and the two UAV image datasets. The experimental results demonstrate that the proposed method can achieve a comparable detection speed but an accuracy superior to those of the six state-of-the-art methods.