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

• Main Authors: Danqing Xu, Yiquan Wu
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Remote Sensing
• Subjects: target detection; remote sensing images; YOLO-V3; feature enhancement; deep learning
• Online Access: https://www.mdpi.com/2072-4292/13/7/1311

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.