Target Detection in Remote Sensing Image Based on Object-and-Scene Context Constrained CNN

Cheng, Bei; Li, Zhengzhou; Xu, Bitong; Dang, Chujia; Deng, Jiaqi

doi:10.1109/lgrs.2021.3087597

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…Ablation experiments are performed on NWPU VHR-10 and HRRSD datasets. Five mainstream methods (SSD [28], FRCNN [26], YOLOv4 [29], DCIFF [44], and SCCM-BR [15]) are introduced for comparative experiments to verify the effectiveness of the proposed method. improved, especially for the track-and-field and basketball court, where the target context is closely related.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

“…Compared to traditional methods, deep learning has offered a rich diversity of remote sensing target detection methodologies and shown significant advantages in recent years [14,15,16,17,18]. Learned features have quickly overtaken manual ones.…”

Section: Figure 1 Representative Detection Problems In Remote Sensing...mentioning

confidence: 99%

“…Cheng et al proposed a one-stage network containing two sub-networks to handle object and scene contexts [15].…”

Section: Contextual Informationmentioning

confidence: 99%

See 2 more Smart Citations

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Ding,

Lyu,

Yan

et al. 2023

cai

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Remote sensing target detection has been widely used in industries. In various application scenarios, complicated contexts may inhibit target identification and reduce detection accuracy, especially in multi-target detection tasks. In this paper, a new remote sensing target detection method based on structural reasoning is proposed to improve target detection performance by integrating inter-object relationships and scene information. Based on inter-object information, a relation structure graph is designed to reduce errors and missed targets. To establish * Corresponding author Remote Sensing Target Detection by Scene and Inter-Object Relations 1405 contextual constraints, semantic is used as a prior information for Bayesian criterion based on scene information. Experiments conducted on HRRSD dataset show that the average accuracy of the proposed method is 10.7 % higher than the state-of-the-art algorithms. The experimental results confirm that the proposed algorithm can achieve significant improvements and adapt to complex scenes in remote sensing by mining contextual information at both feature and semantic levels.

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Section: Experiments Results and Analysismentioning

confidence: 99%

Section: Figure 1 Representative Detection Problems In Remote Sensing...mentioning

confidence: 99%

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Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Ding,

Lyu,

Yan

et al. 2023

cai

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“…Bai et al [29] innovated a time-frequency analysis object detection approach, integrating a discrete wavelet multi-scale attention mechanism to centrally detect object areas. Cheng et al [30] proposed a detection model for remote sensing images incorporating object and scene context constraints. This model utilizes the scene context constraint channel, along with prior information and Bayesian criteria, to enhance the object detection by leveraging comprehensive scene details.…”

Section: Remote Sensing Image Detectionmentioning

confidence: 99%

MFIL-FCOS: A Multi-Scale Fusion and Interactive Learning Method for 2D Object Detection and Remote Sensing Image Detection

Zhang,

Yu,

Hou

2024

Remote Sensing

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Object detection is dedicated to finding objects in an image and estimate their categories and locations. Recently, object detection algorithms suffer from a loss of semantic information in the deeper feature maps due to the deepening of the backbone network. For example, when using complex backbone networks, existing feature fusion methods cannot fuse information from different layers effectively. In addition, anchor-free object detection methods fail to accurately predict the same object due to the different learning mechanisms of the regression and centrality of the prediction branches. To address the above problem, we propose a multi-scale fusion and interactive learning method for fully convolutional one-stage anchor-free object detection, called MFIL-FCOS. Specifically, we designed a multi-scale fusion module to address the problem of local semantic information loss in high-level feature maps which strengthen the ability of feature extraction by enhancing the local information of low-level features and fusing the rich semantic information of high-level features. Furthermore, we propose an interactive learning module to increase the interactivity and more accurate predictions by generating a centrality-position weight adjustment regression task and a centrality prediction task. Following these strategic improvements, we conduct extensive experiments on the COCO and DIOR datasets, demonstrating its superior capabilities in 2D object detection tasks and remote sensing image detection, even under challenging conditions.

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“…Existing methods for small object detection in RSIs mainly focus on two aspects: context information and multi-scale processing [41], [42], [43]. However, these methods overlook a crucial issue, which is the severe loss of feature information for small objects after multiple downsampling operations in RSIs, as well as the inadequate preservation of HR contextual information.…”

Section: B Super Resolution In Object Detectionmentioning

confidence: 99%

Object Detection by Channel and Spatial Exchange for Multimodal Remote Sensing Imagery

Nan,

Zhao,

et al. 2024

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

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Smart satellites and unmanned aerial vehicles (UAVs) are typically equipped with visible light and infrared (IR) spectrum sensors. However, achieving real-time object detection utilizing these multimodal data on such resource-limited devices is a challenging task. This paper proposes HyperYOLO, a realtime lightweight object detection framework for multimodal remote sensing images. First, we propose a lightweight multimodal fusion module named Channel and Spatial Exchange (CSE) to effectively extract complementary information from different modalities. The CSE module consists of two stages: channel exchange and spatial exchange. Channel exchange achieves global fusion by learning global weights to better utilize cross-channel information correlation, while spatial exchange captures details by considering spatial relationships to calibrate local fusion. Second, we propose an effective auxiliary branch module based on the feature pyramid network for super resolution (FPNSR) to enhance the framework's responsiveness to small objects by learning high-quality feature representations. Moreover, we embed a coordinate attention mechanism to assist our network in precisely localizing and attending to the objects of interest. The experimental results show that on the VEDAI remote sensing dataset, HyperYOLO achieves a 76.72% mAP 50 , surpassing the SOTA SuperYOLO by 1.63%. Meanwhile, the parameter size and GFLOPs of HyperYOLO are about 1.34 million (28%) and 3.97 (22%) less than SuperYOLO, respectively. In addition, HyperYOLO has a file size of only 7.3 MB after the removal of the auxiliary FPNSR branch, which makes it easier to deploy on these resource-constrained devices.

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Target Detection in Remote Sensing Image Based on Object-and-Scene Context Constrained CNN

Cited by 9 publications

References 15 publications

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

MFIL-FCOS: A Multi-Scale Fusion and Interactive Learning Method for 2D Object Detection and Remote Sensing Image Detection

Object Detection by Channel and Spatial Exchange for Multimodal Remote Sensing Imagery

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