Efficient Detection in Aerial Images for Resource-Limited Satellites

Li, Leyang; Cao, Guixing; Li, Jun; Yu, Tong

doi:10.1109/lgrs.2020.3046739

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…The extremely large neural network model and high computational workload have prohibited similar schemes like CSFF and CF2PN to be deployed on embedded hardware platforms. In contrast, Simple-CNN [54] and ASSD-lite [55] have used simpler backbone structures and more compact network design. These two methods can then achieve real-time processing speed (around 60 FPS) on desktop GPUs.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Lang

Zhang

et al. 2021

Sensors

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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.

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Section: Comparison With the State-of-the-artmentioning

confidence: 99%

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Lang

Zhang

et al. 2021

Sensors

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show abstract

“…e experiment proved that BBAVectors can achieve better performance of object detection in aerial images. Li et al proposed an efficient detection framework called simple convolutional neural networks (simple-CNNs) in [39], which can be directly applied to real-world applications. In that paper, a new loss function, namely, the change-IOU Loss (CI-Loss), was designed to improve the detection performance with the target position information.…”

Section: Related Workmentioning

confidence: 99%

DAR‐Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images

Wang

2021

Computational Intelligence and Neuroscience

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With the rapid development of deep learning and the wide usage of Unmanned Aerial Vehicles (UAVs), CNN-based algorithms of vehicle detection in aerial images have been widely studied in the past several years. As a downstream task of the general object detection, there are some differences between the vehicle detection in aerial images and the general object detection in ground view images, e.g., larger image areas, smaller target sizes, and more complex background. In this paper, to improve the performance of this task, a Dense Attentional Residual Network (DAR-Net) is proposed. The proposed network employs a novel dense waterfall residual block (DW res-block) to effectively preserve the spatial information and extract high-level semantic information at the same time. A multiscale receptive field attention (MRFA) module is also designed to select the informative feature from the feature maps and enhance the ability of multiscale perception. Based on the DW res-block and MRFA module, to protect the spatial information, the proposed framework adopts a new backbone that only downsamples the feature map 3 times; i.e., the total downsampling ratio of the proposed backbone is 8. These designs could alleviate the degradation problem, improve the information flow, and strengthen the feature reuse. In addition, deep-projection units are used to reduce the impact of information loss caused by downsampling operations, and the identity mapping is applied to each stage of the proposed backbone to further improve the information flow. The proposed DAR-Net is evaluated on VEDAI, UCAS-AOD, and DOTA datasets. The experimental results demonstrate that the proposed framework outperforms other state-of-the-art algorithms.

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Target Detection Model Distillation Using Feature Transition and Label Registration for Remote Sensing Imagery

Zhao

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Deep convolution networks have been widely used in remote sensing target detection for various applications in recent years. Target detection models with many parameters provide better results but are not suitable for resource-constrained devices due to their high computational cost and storage requirements. Furthermore, current lightweight target detection models for remote sensing imagery rarely have the advantages of existing models. Knowledge distillation can improve the learning ability of a small student network from a large teacher network due to acceleration and compression. However, current knowledge distillation methods typically use mature backbones as teacher and student networks are unsuitable for target detection in remote sensing imagery. In this paper, we propose a target detection model distillation (TDMD) framework using feature transition and label registration for remote sensing imagery. A lightweight attention network is designed by ranking the importance of the convolutional feature layers in the teacher network. Multi-scale feature transition based on a feature pyramid is utilized to constrain the feature maps of the student network. A label registration procedure is proposed to improve the TDMD model's learning ability of the output distribution of the teacher network. The proposed method is evaluated on the DOTA and NWPU VHR-10 remote sensing image datasets. The results show that the TDMD achieves a mean Average Precision (mAP) of 75.47% and 93.81% on the DOTA and NWPU VHR-10 datasets, respectively. Moreover, the model size is 43% smaller than that of the predecessor model (11.8 MB and 11.6 MB for the two datasets).

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Efficient Detection in Aerial Images for Resource-Limited Satellites

Cited by 7 publications

References 19 publications

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

DAR‐Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images

Target Detection Model Distillation Using Feature Transition and Label Registration for Remote Sensing Imagery

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