TCANet: Triple Context-Aware Network for Weakly Supervised Object Detection in Remote Sensing Images

Feng, Xiao; Han, Junwei; Yao, Xiwen; Cheng, Gong

doi:10.1109/tgrs.2020.3030990

Cited by 78 publications

(37 citation statements)

References 51 publications

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“…However, as the quantity and quality of remote sensing images increase, the cost of manual annotation will increase exponentially. Therefore, object detection methods based on weakly supervised learning have attracted the attention of some scholars [39]- [41]. At present, most of the existing remote sensing image data sets are labeled with rectangular boxes, while labels with rotated boxes are relatively lacking.…”

Section: Discussionmentioning

confidence: 99%

Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images

Zhu

Zhou

et al. 2021

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

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Aiming to address the problems of arbitrary orientations, large aspect ratios, and dense arrangements in ship detection, an arbitrary-oriented ship detection method based on RetinaNet is proposed. Our proposed method includes a rotated RetinaNet, a refined network, a feature alignment module, and an improved loss function. First, the rotated RetinaNet achieves rotation detection by using a feature pyramid network, rotated anchors, the skew intersection-over-union (IoU), and skew non-maximum suppression (NMS). Then, the refined network and feature alignment module are introduced to achieve better detection accuracy. Finally, to address the boundary discontinuity, the loss function is improved by introducing the IoU constant factor. Considering the problems with the HRSC2016 dataset, we establish a new dataset with more accurate labels and more images and object samples. Through an ablation study, we thoroughly analyze the validity of the proposed rotated RetinaNet, feature alignment module, and improved loss function. The experimental results show that our method is superior to other state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images

Zhu

Zhou

et al. 2021

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

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“…Different from imagebased task, e.g. detection [12][13][14]48], localization [15,55,59] and segmentation [56], it is crucial to model temporal dependencies for online action detection. Existing works rely on recurrent networks, including both LSTMbased methods [9,45,50] and GRU-based methods [11].…”

Section: Related Workmentioning

confidence: 99%

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars

Yang¹,

Han²,

Zhang³

2022

Preprint

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Online action detection has attracted increasing research interests in recent years. Current works model historical dependencies and anticipate the future to perceive the action evolution within a video segment and improve the detection accuracy. However, the existing paradigm ignores category-level modeling and does not pay sufficient attention to efficiency. Considering a category, its representative frames exhibit various characteristics. Thus, the category-level modeling can provide complimentary guidance to the temporal dependencies modeling. This paper develops an effective exemplar-consultation mechanism that first measures the similarity between a frame and exemplary frames, and then aggregates exemplary features based on the similarity weights. This is also an efficient mechanism, as both similarity measurement and feature aggregation require limited computations. Based on the exemplarconsultation mechanism, the long-term dependencies can be captured by regarding historical frames as exemplars, while the category-level modeling can be achieved by regarding representative frames from a category as exemplars. Due to the complementarity from the categorylevel modeling, our method employs a lightweight architecture but achieves new high performance on three benchmarks. In addition, using a spatio-temporal network to tackle video frames, our method makes a good trade-off between effectiveness and efficiency. Code is available at https://github.com/VividLe/Online-Action-Detection.

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“…Also, some methods were proposed for weakly supervised object detection (WSOD) in remote sensing images [21,23,[45][46][47][48]. For instance, [21] proposed a coupled weakly supervised learning framework for aircraft detection.…”

Section: Object Detection In Remote Sensingmentioning

confidence: 99%

Feature Enhancement Network for Object Detection in Optical Remote Sensing Images

Cheng

Lang

et al. 2021

J Remote Sens

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Automatic and robust object detection in remote sensing images is of vital significance in real-world applications such as land resource management and disaster rescue. However, poor performance arises when the state-of-the-art natural image detection algorithms are directly applied to remote sensing images, which largely results from the variations in object scale, aspect ratio, indistinguishable object appearances, and complex background scenario. In this paper, we propose a novel Feature Enhancement Network (FENet) for object detection in optical remote sensing images, which consists of a Dual Attention Feature Enhancement (DAFE) module and a Context Feature Enhancement (CFE) module. Specifically, the DAFE module is introduced to highlight the network to focus on the distinctive features of the objects of interest and suppress useless ones by jointly recalibrating the spatial and channel feature responses. The CFE module is designed to capture global context cues and selectively strengthen class-aware features by leveraging image-level contextual information that indicates the presence or absence of the object classes. To this end, we employ a context encoding loss to regularize the model training which promotes the object detector to understand the scene better and narrows the probable object categories in prediction. We achieve our proposed FENet by unifying DAFE and CFE into the framework of Faster R-CNN. In the experiments, we evaluate our proposed method on two large-scale remote sensing image object detection datasets including DIOR and DOTA and demonstrate its effectiveness compared with the baseline methods.

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TCANet: Triple Context-Aware Network for Weakly Supervised Object Detection in Remote Sensing Images

Cited by 78 publications

References 51 publications

Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images

Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars

Feature Enhancement Network for Object Detection in Optical Remote Sensing Images

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