R4 Det: Refined single-stage detector with feature recursion and refinement for rotating object detection in aerial images

Sun, Peng; Zheng, Yongbin; Zhou, Zhifeng; Wang, Xu; Ren, Qiang

doi:10.1016/j.imavis.2020.104036

Cited by 28 publications

(9 citation statements)

References 13 publications

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“…This makes target tracking difficult. In rotating target detection technology, one-stage detector [1,2] and two-stage detector [3,4] both have achieved good accuracy and speed. However, these mainstream target detectors can only detect all objects with corresponding features.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] FMT Selector: Fourier‐Mellin Transformer with High Speed Rotating in Ship Target Detection and Tracking Based on Internet of Things and Wireless Sensor Network

Qiu

Wang

2023

Journal of Sensors

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The terminal guidance tracking always has high speed rotating and scale changing. It makes a large margin of error in current tracking algorithms, which can transmit wrong position in the wireless sensor networks of the Internet of Things. In this paper, we propose a selector based on Fourier-Mellin transformer, which can accurately track the target with high speed rotating. Firstly we use existing detector to get all proposal target per frame. Then, we use Fourier-Mellin matching to select the best target in the new frame. Our dataset is the ship video which is created by HRSC2016 in rotating. Experimental results show that the accuracy of this method is up to 89.8%. Compared with other traditional tracking algorithms, it has a leap forward. The FMT selector can be applied to the back end of the detector, and it can achieve a feasible effect in the field of terminal guidance tracking.

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Section: Introductionmentioning

confidence: 99%

[Retracted] FMT Selector: Fourier‐Mellin Transformer with High Speed Rotating in Ship Target Detection and Tracking Based on Internet of Things and Wireless Sensor Network

Qiu

Wang

2023

Journal of Sensors

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“…It is widely applied to aerial and satellite images to detect objects. Sun et al have developed a new end-to-end detector, R 4 (“Refined Single-stage detector with feature Recursion and Refinement for Rotating objects”), that uses RetinaNet as a base network to detect the objects from dense distribution, larger aspect ratio, and category imbalance [ 163 ].…”

Section: Techniques For Object Detectionmentioning

confidence: 99%

Tools, techniques, datasets and application areas for object detection in an image: a review

Kaur

Singh

2022

Multimed Tools Appl

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Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

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“…R3Det [51] adopts cascade regression and refined box re-encoding module with horizontal anchors to achieve state-of-the-art performance. R4Det [52] proposes a refined single-stage detector with feature recursion and refinement for rotating objects. Although our proposed oriented detection framework also adopts a cascaded structure, it differs in several important aspects.…”

Section: Multi-stage Object Detectionmentioning

confidence: 99%

Learning Higher Quality Rotation Invariance Features for Multioriented Object Detection in Remote Sensing Images

Zhang

Xiong

Xiao

et al. 2021

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

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Multi-oriented object detection, an important yet challenging task because of the bird's-eye-view perspective, complex background and densely packed objects, is in the spotlight of detection in remote sensing images. Although existing methods have recently experienced substantial progress based on oriented head, they learn little about essential rotation invariance of the objects. In this article, a novel framework is proposed that can learn high-quality rotation invariance features of the multi-oriented objects by three measures. Given a remote sensing image, the MSFF module first merges the global semantic segmentation features predicted by the semantic segmentation branch and the multi-scale features extracted by the backbone with FPN in order to distinguish complex background. Then the discriminative features are used by rotation mainstream, whose structure is similar to Cascade R-CNN and can extract higher-quality rotation invariance features and predict more accurate location information by adaptively adjusting the distribution of the samples through progressive IoU thresholds. And in order to improve the performance of mainstream to predict more accurate oriented bounding box, the horizontal tributaries that can fully leverage the reciprocal relationship between the oriented detection and horizontal detection were added to the latter two stages. Extensive experiments on three public datasets for remote sensing images i.e. Gaofen Airplane, HRSC2016 and DOTA demonstrate that without bells and whistles, the proposed method achieves superior performances compared with the existing state-of-the-art methods for multi-oriented detection. Moreover, our overall system achieves 59.264% mAP of airplane Detection in 2020 Gaofen challenge, ranking 3rd in the final.

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R4 Det: Refined single-stage detector with feature recursion and refinement for rotating object detection in aerial images

Cited by 28 publications

References 13 publications

[Retracted] FMT Selector: Fourier‐Mellin Transformer with High Speed Rotating in Ship Target Detection and Tracking Based on Internet of Things and Wireless Sensor Network

[Retracted] FMT Selector: Fourier‐Mellin Transformer with High Speed Rotating in Ship Target Detection and Tracking Based on Internet of Things and Wireless Sensor Network

Tools, techniques, datasets and application areas for object detection in an image: a review

Learning Higher Quality Rotation Invariance Features for Multioriented Object Detection in Remote Sensing Images

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