Abstract
Deep learning-based object detection in remote sensing images is an important yet challenging task due to a series of difficulties, such as complex geometry scene, dense target quantity, and large variant in object distributions and scales. Moreover, algorithm designers also have to make a trade-off between model’s complexity and accuracy to meet the real-world deployment requirements. To deal with these challenges, we proposed a lightweight YOLO-like object detector with the ability to detect objects in remote sensing images with high speed and high accuracy. The detector is constructed with efficient channel attention layers to improve the channel information sensitivity. Differential evolution was also developed to automatically find the optimal anchor configurations to address issue of large variant in object scales. Comprehensive experiment results show that the proposed network outperforms state-of-the-art lightweight models by 5.13% and 3.58% in accuracy on the RSOD and DIOR dataset, respectively. The deployed model on an NVIDIA Jetson Xavier NX embedded board can achieve a detection speed of 58 FPS with less than 10W power consumption, which makes the proposed detector very suitable for low-cost low-power remote sensing application scenarios.
Highlights
Garcia Rodriguez and AlbertoWith the rapid development of satellite and imaging technology, optical remote sensing images with high spatial resolution are obtained more conveniently than ever before [1]
We have proposed an efficient lightweight object detector for remote sensing images based on deep convolutional neural networks
To achieve the best balance between detection speed and accuracy, we first designed an improved YOLOv4-like backbone network with three prediction layers to alleviate the problem of multi-scale object detection
Summary
Garcia Rodriguez and AlbertoWith the rapid development of satellite and imaging technology, optical remote sensing images with high spatial resolution are obtained more conveniently than ever before [1]. Deep neural network-based schemes have shown superior performance over traditional approaches [6,7]. These schemes can be divided into two major categories: (1) one-stage neural network which adopts a fully convolutional architecture that outputs a fixed number of predictions on the grid, such as SSD [8], YOLO [9], and M2Det [10], and (2) two-stage network that leverages a proposal network to find regions of interest that have a high probability to contain an object and a second network to get the classification score and spatial offsets, such as FPN [11] and Faster
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