RFSOD: a lightweight single-stage detector for real-time embedded applications to detect small-size objects

Amudhan, A. N.; Vrajesh, Shah Rutvik; Sudheer, A. P.; Lijiya, A

doi:10.1007/s11554-021-01170-3

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…(AI20) [127] 18.6 -ViT-FRCNN (arXiv20) [107] -17.8 DETR (ECCV20) [110] -21.9 DETR-DC5 -23.7 Deformable DETR (arXiv20) [115] -26.4 Two Stage Deformable DETR (arXiv20) [115] -28.8 Full Deformable DETR (arXiv20) [115] -34.4 ATSS (CVPR20) [281] -33.2 YOLOv5s [37] -18.8 TSD (CVPR20) [282] -33.8 STDnet-C3 (EAAI20) [137] 11.4 + 5.5 + YOLOS (NIPS21) [108] -19.5 UP-DETR (CVPR21) [117] -20.8 SOF-DETR [116] -21.7 ViDT w.o. Neck (arXiv21) [109] -21.9 ViDT (arXiv21) [109] -30.6 SMCA (ICCV21) [283] 22.8 -DETR-GQPos (arXiv21) [113] 23.1 -DETR-GQPos-SiA (arXiv21) [113] 24.4 -FP-DETR (ICLR22) [118] -27.5 SODNet (RS22) [125] -20.1 RFSOD (RTIP22) [120] 59.09 * -RFSODTL (RTIP22) [120] 56.42 * -QueryDet (CVPR22) [136] -25.24 RESC (NCA22) [119] -26.2 D 2 ETR (arXiv22) [112] -22 Deformable D 2 ETR (arXiv22) [112] -31.7 TABLE 6: Detection performance (%) for small-scale objects on USC-GRAD-STDdb video dataset [137]. +k indicates that the anchors were defined by the k-means algorithm and the " * " indicates that they were run on Caffe2 framework.…”

Section: Generic Sod Performance Resultsmentioning

confidence: 99%

“…Multi-scale feature learning is one of the most common approaches for SOD, and several architectures have been developed to support it. Amudhan et al [120] introduced RFSOD, a lightweight single-stage detector that can be used in embedded systems for real time applications. RFSOD's architecture is similar to that of the YOLO detector, and uses 3 × 3 and 1 × 1 convolutions for lightweight detection.…”

Section: Feature Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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Section: Generic Sod Performance Resultsmentioning

confidence: 99%

Section: Feature Learningmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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show abstract

“…1 the basic design of a twostage detector [26]. As a result, one-stage detectors [27]may be employed in real-time devices since they suggest predicted boxes straight from input pictures without requiring a region proposal phase. One-stage detectors' fundamental construction is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Deep Learning-Based Object Identification

Kolluri

Das

2022

IJRITCC

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Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study.

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$$\hbox {ABDF}^{2}$$-Net: an adaptive bi-directional features fusion network for real-time detection of threat object

Wei

Zhu²,

Yu³

et al. 2022

J Real-Time Image Proc

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RFSOD: a lightweight single-stage detector for real-time embedded applications to detect small-size objects

Cited by 8 publications

References 50 publications

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

An Evaluation of Deep Learning-Based Object Identification

$$\hbox {ABDF}^{2}$$-Net: an adaptive bi-directional features fusion network for real-time detection of threat object

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