Single-Shot Balanced Detector for Geospatial Object Detection

Liu, Yanfeng; Li, Qiang; Yuan, Yuan; Wang, Qi

doi:10.1109/icassp43922.2022.9746853

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…With the purpose of further confirming the effectiveness of the proposed method, this study compares the performance of the proposed model with the classic detection model on the HRRSD dataset. Specifically, the compared methods include YOLOv3, FCOS, FRCNN, RetinaNet, HIE-Det [66], S2BDet [67], MSE-Net [68], and GLFPN [69]. Table 3 presents the findings of the performance comparison.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Capsule-inferenced Object Detection for Remote Sensing Images

Han

Meng

Tang

et al. 2023

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

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Frequent and accurate object detection based on remote sensing images can effectively monitor dynamic objects on the Earth's surface. While the Detection Transformer (DETR) offers a simple encoder-decoder structure and a direct set prediction approach to object detection, it falls short in complex remote sensing scenes where entity information and relative positions between objects are critical to target reasoning. Notably, the DETR model's feedforward neural network (FFN) relies on weighted summation for target reasoning, disregarding interactive feature information, which is a major factor affecting detection effectiveness. To address these shortcomings, we propose a DETR-based detection model called (CI_DETR), which uses capsule inference to improve remote sensing object detection. Our approach adds a Multi-level Feature Fusion module to the DETR network, allowing the network to learn how to spatially alter features at different levels, preserving only beneficial information for combination. In addition, we introduce a capsule reasoning module to mine entity information during inference and more effectively model the hierarchical correlation of internal knowledge representation in the neural network, consistent with the thinking model of the human brain. Lastly, we employ a sausage model to measure the similarities and differences of capsules, projecting them onto a curved surface for nonlinear function approximation and maximum preservation of the local responsiveness of capsule entities. Our experiments on public datasets confirm the superior detection performance of our proposed algorithm relative to many current detectors.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Capsule-inferenced Object Detection for Remote Sensing Images

Han

Meng

Tang

et al. 2023

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

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“…With the development of deep learning [2–6], frameworks applying deep convolutional neural networks (CNNs) can detect fires more accurately and efficiently [7–9, 10–13]. Muhammad et al.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of deep learning [2][3][4][5][6], frameworks applying deep convolutional neural networks (CNNs) can detect fires more accurately and efficiently [7][8][9][10][11][12][13]. Muhammad et al propose a computationally efficient CNN structure to localize, and understand the semantic of fire scenes [8].…”

mentioning

confidence: 99%

Dynamic attention network for flame detection

Fang

Zhang

et al. 2023

Electronics Letters

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Recently, flame detection has attracted great attention. However, existing methods have the issues of low detection rates, high false alarm rates, and lack of smoke anti-interference ability. In this letter, a novel dynamic attention-based network (DANet) is proposed for autonomous flame detection in various scenarios. To mitigate the disturbance of smoke in images, a dynamic attention strategy is proposed to discover the potential features among scale-awareness and spatial-awareness. Then, based on dynamic attention module, a decoupled detection head is presented, which can predict category, regression, and object score independently to boost the performance. A self-contained challenging flame dataset, which is multi-scene, multiscale, and multi-interference informative is constructed to evaluate the proposed model and organize the experiments. Extensive ablation and comparison studies on self-labelled dataset reveal the effectiveness of the proposed dynamic attention-based network.

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An Improved Framework for Airport Detection Under the Complex and Wide Background

Cheng

et al. 2022

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

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As an important transportation facility, airports substantially affect the economic lives of people. However, the full extraction of airports located in a vast area is concerning. The size of an airport in a previous wide area detection framework is relatively large and has a strong saliency in remote sensing images, whereas the contradiction between a complex geographical background and a small airport size has yet to be resolved. In this study, we propose a set of automatic detection frameworks to realize efficient detection for various airports in nine Indian states/union territories under the condition that only runway samples are labeled. A preliminary extraction of runway features is performed with a high F1 and recall rate, and teacher nodes judge and guide the results. Next, the output is connected to classification and segmentation for outlier elimination and pixel extraction to locate the runways. For the study area, the proposed framework airport retention rate (RR) is 92.7%, with the false alarm reduction rate(FARR) reduced by a maximum of 95.3%. A total of 192 airports are discovered, and the effective airport growth rate(GR) is 47.4%. Compared to previous work, RR, GR, FARR, and run efficiency increased by 2.2%, 16.0%, 4.5%, and 432.5%, respectively, with more small-and medium-sized airports detected. Furthermore, the framework is tested in Japan, and 155 airports are detected. Thus, the proposed framework effectively improves detection capability for small-and medium-sized airports in large-scale areas and updates the airport database.

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Single-Shot Balanced Detector for Geospatial Object Detection

Cited by 13 publications

References 19 publications

Capsule-inferenced Object Detection for Remote Sensing Images

Capsule-inferenced Object Detection for Remote Sensing Images

Dynamic attention network for flame detection

An Improved Framework for Airport Detection Under the Complex and Wide Background

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