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CDD-Net: A Context-Driven Detection Network for Multiclass Object Detection
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DOI：10.1109/LGRS.2020.3042465.png)
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Abstract
En 中文
Unlike object detection in natural images that usually achieved great success, remote sensing imagery has its own challenges to detect and localize multiclass objects, such as large-scale change, uncertain direction, and high density. The context information of the objects is very worthwhile for solving these challenges in remote sensing images. In this letter, we propose a context-driven detection network (CDD-Net) to improve the accuracy of multiclass object detection in remote sensing images. For capturing the local neighboring objects and features, a local context feature network (LCFN) is proposed to learn the local context of the region of interest. Meanwhile, a hybrid attention pyramid network (HAPN) is designed, which can steer the focus to more valuable features. The HAPN inserts a squeeze and excitation block (SEB) and three asymmetric convolution blocks (ACBs) in the feature pyramid network (FPN). The experimental results over the DOTA-v1.5 data set demonstrate that the proposed CDD-Net yields promising results.
Keywords:
Feature extraction
Remote sensing
Convolution
Object detection
Proposals
Robustness
Optical imaging
Hybrid attention
local context
object detection
remote sensing imagery
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