Abstract
In recent years, deep learning has led to a remarkable breakthrough in object detection in remote sensing images. In practice, two-stage detectors perform well regarding detection accuracy but are slow. On the other hand, one-stage detectors integrate the detection pipeline of two-stage detectors to simplify the detection process, and are faster, but with lower detection accuracy. Enhancing the capability of feature representation may be a way to improve the detection accuracy of one-stage detectors. For this goal, this paper proposes a novel one-stage detector with enhanced capability of feature representation. The enhanced capability benefits from two proposed structures: dual top-down module and dense-connected inception module. The former efficiently utilizes multi-scale features from multiple layers of the backbone network. The latter both widens and deepens the network to enhance the ability of feature representation with limited extra computational cost. To evaluate the effectiveness of proposed structures, we conducted experiments on horizontal bounding box detection tasks on the challenging DOTA dataset and gained 73.49% mean Average Precision (mAP), achieving state-of-the-art performance. Furthermore, our method ran significantly faster than the best public two-stage detector on the DOTA dataset.
Highlights
Object detection in optical remote sensing images is widely applied into many key fields such as environmental monitoring, geological hazard detection, precision agriculture, etc. [1]
All ablation experiments are conducted via cropped val dataset, while the final detector is evaluated via official test dataset for comparison with state-of-the-art detectors
In order to improve the performance while maintaining the high detection speed, we choose the lightweight network VGG16 as our backbone network
Summary
Object detection in optical remote sensing images is widely applied into many key fields such as environmental monitoring, geological hazard detection, precision agriculture, etc. [1]. The conventional analytical methods are hard to meet growing diversified needs. In this case, the introduction of Convolutional Neural Networks (CNN) [2], the performance of which has been widely proved on general object detection [3,4], attracts growing attention from remote sensing field. The introduction of Convolutional Neural Networks (CNN) [2], the performance of which has been widely proved on general object detection [3,4], attracts growing attention from remote sensing field These detectors can be divided into two main categories: the two-stage detection and the one-stage detection framework
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