FE-YOLO: A Feature Enhancement Network for Remote Sensing Target Detection

Remote Sensing

Danqing Xu¹  Yiquan Wu¹ 

[1] College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;

关键词: target detection;    remote sensing images;    YOLO-V3;    feature enhancement;    deep learning;   

DOI  :  10.3390/rs13071311

来源: DOAJ

【 摘 要 】

In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.

【 授权许可】

Unknown