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Open AccessArticle

VaryBlock: A Novel Approach for Object Detection in Remote Sensed Images

by Heng Zhang *,†, Jiayu Wu, Yanli Liu *,† and Jia Yu
School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(23), 5284;
Received: 21 October 2019 / Revised: 20 November 2019 / Accepted: 26 November 2019 / Published: 30 November 2019
(This article belongs to the Special Issue Deep Learning-Based Soft Sensors)
In recent years, the research on optical remote sensing images has received greater and greater attention. Object detection, as one of the most challenging tasks in the area of remote sensing, has been remarkably promoted by convolutional neural network (CNN)-based methods like You Only Look Once (YOLO) and Faster R-CNN. However, due to the complexity of backgrounds and the distinctive object distribution, directly applying these general object detection methods to the remote sensing object detection usually renders poor performance. To tackle this problem, a highly efficient and robust framework based on YOLO is proposed. We devise and integrate VaryBlock to the architecture which effectively offsets some of the information loss caused by downsampling. In addition, some techniques are utilized to facilitate the performance and to avoid overfitting. Experimental results show that our proposed method can enormously improve the mean average precision by a large margin on the NWPU VHR-10 dataset. View Full-Text
Keywords: remote sensing; object detection; YOLO; VaryBlock remote sensing; object detection; YOLO; VaryBlock
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Zhang, H.; Wu, J.; Liu, Y.; Yu, J. VaryBlock: A Novel Approach for Object Detection in Remote Sensed Images. Sensors 2019, 19, 5284.

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