AOPDet: Automatic Organized Points Detector for Precisely Localizing Objects in Aerial Imagery

Zhu, Zicong; Sun, Xian; Diao, Wenhui; Chen, Kaiqiang; Xu, Guangluan; Fu, Kun

doi:10.1109/tgrs.2021.3093557

Cited by 15 publications

(5 citation statements)

References 58 publications

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“…Problem solved Optimization Strategies single-stage detector [41] Boundary-arbitrary discontinuity FAB+DRBs+CRB DA-Net [42] Boundary-arbitrary discontinuity RFS+RFA MGAR [43] Vague angle representation CAC+FAR+IFL OrtDet [44] Angular periodicity Mean rotational accuracy (mRP) AProNet [45] Angular periodicity Axis-based Angle Learning Arbitrary orientation regression [46] Arbitrary angle Adaptive target orientation regression CFC-Net [47] Arbitrary angle Rotation anchor refinement module Angle encoding mechanism [48] Arbitrary angle Aspect ratio-based bidirectional coding label RH-RCNN [49] Arbitrary angle Distinguish tilted targets AOPDet [50] Rotation object representation Non-sequential angular representation ACE [51] Rotation object representation Directed quadrilateral box Faster R-CNN-based [52] Rotated Region Proposal Majority voting strategy RiDOP [53] Rotated Region Proposal Sliding only two vertices R-RCNN [54] Rotated Region Proposal Directional RoI pooling operation Point RCNN [55] Rotated Region Proposal PointRPN module generates RRoI New anchor-free detector [56] Arbitrary angle Center Boundary Dual-Attention (CBDA) AOPG [57] Arbitrary angle Generates orientation boxes in an anchor-free manner AOPG+FRIoU [58] Arbitrary angle Focal Rotated Intersection over Union(FRIoU) R2YOLOX [59] Arbitrary angle Refined Rotation Module (RRM) DARDet [60] Arbitrary angle ACM+PIoU ADT-Det [61] Inadequate expression of features Feature Pyramid Transformer (FPT) AFA-FPN [62] Inadequate expression of features Employs RROI to rotate the horizontal frames RINet [63] Inadequate expression of features Flexible multi-branch online detector improvement FoRDet [64] Inadequate expression of features Foreground Relationship module (FRL)…”

Section: Methodsmentioning

confidence: 99%

“…By doing so, it enhances the detection accuracy specifically for square-like objects. The Automatic Organization Point Detector [50] (AOPDet) uses a novel rotating object representation called non-sequential angular representation to obtain accurate localization results. This method addresses the problem of blurred supervision due to inappropriate rotating target representation.ACE [51] detection method evolves the axial bounding box into a directed quadrilateral box with the assistance of dynamically collected contour information.…”

Section: Methodsmentioning

confidence: 99%

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A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Wang

Chen

2023

Preprint

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Small object detection poses a formidable challenge in the field of computer vision, particularly when it comes to analyzing aerial remote sensing images. Despite the rapid development of deep learning and significant progress in detection techniques in natural scenes, the migration of these algorithms to aerial images has not met expectations. This is primarily due to limitations in imaging acquisition conditions, including small target size, viewpoint specificity, background complexity, as well as scale and orientation diversity. Although the increasing application of deep learning-based algorithms to overcome these problems, few studies have summarized the optimization of different deep learning strategies used for small target detection in aerial images. Therefore, this paper aims to explore the application of deep learning methods for small object detection in aerial images. The primary challenges in small object detection in aerial images will be summarized. Next, a meticulous analysis and categorization of the prevailing deep learning optimization strategies employed to surmount the challenges encountered in aerial image detection is undertaken. Following that, we provide a comprehensive presentation of the object detection datasets utilized in aerial remote sensing images, along with the evaluation metrics employed. Additionally, we furnish experimental data pertaining to the currently proposed detection algorithms. Finally, the advantages and disadvantages of various optimization strategies and potential development trends are discussed. Hopefully, it can provide a reference for researchers in this field.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Wang

Chen

2023

Preprint

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“…A directional R-CNN network is designed specifically for rotating-target detection, which considers the speed requirements while retaining the strong detection accuracy advantage of the two-stage target detection network [30]. Zhu et al [31] proposed an Automatic Organized Points Detector (AOPDet), which derives precise localization results by applying a novel rotating-object representation, and an Automatic Organization Mechanism (AOM) technique is designed to guide the model to automatically organize points to object corners. Generally, the precision of rotating-object detectors is still less than that of horizontal-object detectors, especially for small targets.…”

Section: Related Workmentioning

confidence: 99%

Double Augmentation: A Modal Transforming Method for Ship Detection in Remote Sensing Imagery

Mou,

Fan,

Jiang

et al. 2024

Remote Sensing

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Ship detection in remote sensing images plays an important role in maritime surveillance. Recently, convolution neural network (CNN)-based methods have achieved state-of-the-art performance in ship detection. Even so, there are still two problems that remain in remote sensing. One is that the different modal images observed by multiple satellite sensors and the existing dataset cannot satisfy network-training requirements. The other is the false alarms in detection, as the ship target is usually faint in real view remote sensing images and many false-alarm targets can be detected in ocean backgrounds. To solve these issues, we propose a double augmentation framework for ship detection in cross-modal remote sensing imagery. Our method can be divided into two main steps: the front augmentation in the training process and the back augmentation verification in the detection process; the front augmentation uses a modal recognition network to reduce the modal difference in training and in using the detection network. The back augmentation verification uses batch augmentation and results clustering to reduce the rate of false-alarm detections and improve detection accuracy. Real-satellite-sensing experiments have been conducted to demonstrate the effectiveness of our method, which shows promising performance in quantitative evaluation metrics.

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“…achieve aircraft detection in remote sensing images by predicting anchor box belonging to the target. Some methods predict key points belonging to the bounding box for aircraft detection, such as [10], However, there are dense and symmetrically distributed aircraft in remote sensing images, and clustering these key points is difficult.…”

Section: A Aircraft Detection In Remote Sensing Imagesmentioning

confidence: 99%

A Method for Detecting Aircraft Small Targets in Remote Sensing Images by Using CNNs Fused With Handcrafted Features

Yu,

Zhi,

Zhang

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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Aircraft target detection is a challenging task in remote sensing images, especially for aircraft small target detection. The most advanced object detection framework currently processes all information in the image uniformly through a deep neural network. In the past, in the process of detecting aircraft small targets, the feature extraction process was carefully designed, and hand-crafted features were derived from expert knowledge or historical data, which included prior knowledge that was conducive to object detection. Embedding prior features into deep neural networks can enhance the saliency of target information, improve the detection performance of the model. Accordingly, this paper proposes a Hand-crafted Feature Fusion Stream (HFFS) for embedding prior knowledge. We obtain hand-crafted features based on the grayscale co-occurrence matrix and edge extraction operator, and generate an attention map in deep convolutional neural networks (CNNs) to achieve the fusion of hand-crafted feature maps and high-level feature maps in deep convolutional networks. The experimental results show that using HFFS on the baseline model improves the detection performance of the model for aircraft small targets. Compared with the baseline model, our detection model achieves improvements of 1.1% AR, 1.6%

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AOPDet: Automatic Organized Points Detector for Precisely Localizing Objects in Aerial Imagery

Cited by 15 publications

References 58 publications

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

A Survey of Small Object Detection Based on Deep Learning in Aerial Images

Double Augmentation: A Modal Transforming Method for Ship Detection in Remote Sensing Imagery

A Method for Detecting Aircraft Small Targets in Remote Sensing Images by Using CNNs Fused With Handcrafted Features

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