RRNet: A Hybrid Detector for Object Detection in Drone-Captured Images

Chen, Changrui; Zhang, Yu; Lv, Qingxuan; Wei, Shuo; Wang, Xiaorui; Sun, Xin; Dong, Junyu

doi:10.1109/iccvw.2019.00018

Cited by 124 publications

(71 citation statements)

References 10 publications

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“…Through training with these augmented images, the baseline model showed the significant performance improvement. Also, Chen et al [62] applied an adaptive augmentation method called AdaResampling to improve the model performance, where there are two significant issues with the regular augmentation methods, background and scale mismatch. To address the issues, AdaResampling applied a pretrained segmentation network during the augmentation phase to produce a segmented road map.…”

Section: ) Image Augmentationmentioning

confidence: 99%

A Survey of Deep Learning-Based Object Detection Methods and Datasets for Overhead Imagery

Kang

Tariq

et al. 2022

IEEE Access

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Section: ) Image Augmentationmentioning

confidence: 99%

A Survey of Deep Learning-Based Object Detection Methods and Datasets for Overhead Imagery

Kang

Tariq

et al. 2022

IEEE Access

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“…Similar to the traditional cut and paste [23] augmentation technique, one prior work was proposed to solve the class imbalance problem [24]. Another study proposed RRNet [25] to improve a augmentation method. There is also a study that detects objects in cluster units for efficient detection of dense objects [3].…”

Section: Related Workmentioning

confidence: 99%

Robust detection of small and dense objects in images from autonomous aerial vehicles

Lee

YOO

Kim

et al. 2021

Electronics Letters

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Aerial images obtained from autonomous aerial vehicles have lots of small and densely distributed objects because of the capture distance. This paper proposes a deep neural network architecture and training/inference techniques for robust detection of objects in the aerial images. Based on cascade R-CNN, the proposed model adopts the recursive feature pyramid and switchable atrous convolution for robust detection of dense objects. A patch-level division and multi-scale inference techniques are applied to effectively detect small objects. The results show that the proposed approach achieves the highest performance on the VisDrone test-dev dataset, in the official ECCV VisDrone2020-DET challenge.

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“…Reference [ 37 ] integrated the overall and partial fusion strategy with a progressive network with varying scales to fullfill detection in a more accurate manner. In [ 7 ], an anchor-free method was introduced. Compared to the typical method based on center point prediction, the scale of the object needs to be regressed twice to obtain a more accurate bounding box.…”

Section: Related Workmentioning

confidence: 99%

“…Most UAV visible image object detection algorithms are based on widely used and universally structured methods, such as Faster RCNN or SSD, which target the small scale and dense distribution of UAV image objects, either by complicating the network structure or the detection process [ 6 , 7 , 8 , 9 ], or by introducing novel ways of data augmentation [ 7 , 10 ], ultimately making the algorithms perform well on UAV datasets. Typically, the optimisation goal of these algorithms is to improve accuracy as much as possible, with less consideration given to efficiency, and the few fast algorithms are only somewhat faster relative to their predecessors, falling far short of the standard of real-time.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images

Jiang

et al. 2021

Sensors

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Unmanned Aerial Vehicles (UAVs) can serve as an ideal mobile platform in various situations. Real-time object detection with on-board apparatus provides drones with increased flexibility as well as a higher intelligence level. In order to achieve good detection results in UAV images with complex ground scenes, small object size and high object density, most of the previous work introduced models with higher computational burdens, making deployment on mobile platforms more difficult.This paper puts forward a lightweight object detection framework. Besides being anchor-free, the framework is based on a lightweight backbone and a simultaneous up-sampling and detection module to form a more efficient detection architecture. Meanwhile, we add an objectness branch to assist the multi-class center point prediction, which notably improves the detection accuracy and only takes up very little computing resources. The results of the experiment indicate that the computational cost of this paper is 92.78% lower than the CenterNet with ResNet18 backbone, and the mAP is 2.8 points higher on the Visdrone-2018-VID dataset. A frame rate of about 220 FPS is achieved. Additionally, we perform ablation experiments to check on the validity of each part, and the method we propose is compared with other representative lightweight object detection methods on UAV image datasets.

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RRNet: A Hybrid Detector for Object Detection in Drone-Captured Images

Cited by 124 publications

References 10 publications

A Survey of Deep Learning-Based Object Detection Methods and Datasets for Overhead Imagery

A Survey of Deep Learning-Based Object Detection Methods and Datasets for Overhead Imagery

Robust detection of small and dense objects in images from autonomous aerial vehicles

Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images

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