Abstract
Aiming at the problem of insufficient representation ability of weak and small objects and overlapping detection boxes in airplane object detection, an effective airplane detection method in remote sensing images based on multilayer feature fusion and an improved nonmaximal suppression algorithm is proposed. Firstly, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airplane images using a limited amount of data. Then, the L2 norm normalization, feature connection, scale scaling, and feature dimension reduction are introduced to achieve effective fusion of low- and high-level features. Finally, a nonmaximal suppression method based on a soft decision function is proposed to solve the overlap problem of detection boxes. The experimental results show that the proposed method can effectively improve the representation ability of weak and small objects, as well as quickly and accurately detect airplane objects in the airport area.
Highlights
With the rapid development of high-resolution satellite remote sensing technology, the resolution of remote sensing images has become higher, and the acquisition method has become more convenient and diversified, facilitating the detection of objects in remote sensing images [1,2]
The experimental environment included an i7-7700 processor running at 3.6 GHz, 16G of memory, and an NVIDIA GTX1060
The rough and high-level semantic in the high layer feature is favorable for object classification, while precise and high-resolution in the low layer feature is favorable for object positioning
Summary
With the rapid development of high-resolution satellite remote sensing technology, the resolution of remote sensing images has become higher, and the acquisition method has become more convenient and diversified, facilitating the detection of objects in remote sensing images [1,2]. Due to the uncertainty associated with the type, position, and dimension of an airplane and a complex background, airplane detection is always a challenging research topic. To address this challenge, new methods are proposed constantly; they are divided into conventional and deep learning methods. Bo et al [4] proposed a region segmentation-based airplane detection method. Liu et al [5] proposed an airplane recognition method based on coarse-to-fine edge detection
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