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A Survey of Deep Learning-based Object Detection Methods and Datasets for Overhead Imagery
2022
IEEE Access
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We thank Jin Yong Park for reviewing the earlier version of the draft, and providing helpful and insightful comments. ...
Citation information: DOI 10.1109/ACCESS.2022.3149052, IEEE Access Kang et al.: A Survey of Deep Learning-based Object Detection Methods and Datasets for Overhead Imagery B. ...
Citation information: DOI 10.1109/ACCESS.2022.3149052, IEEE Access Kang et al.: A Survey of Deep Learning-based Object Detection Methods and Datasets for Overhead Imagery gion. ...
doi:10.1109/access.2022.3149052
fatcat:iwvyg7qf6jgntgrwk4bmonrd5m
Towards Large-Scale Small Object Detection: Survey and Benchmarks
[article]
2022
arXiv
pre-print
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In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. ...
Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field. ...
ACKNOWLEDGMENTS We thank Peter Kontschieder for the constructive discussions and feedback, as well as their high-quality Mapillary Vistas Dataset. ...
arXiv:2207.14096v2
fatcat:3pdbdoqevbgm5gmcropomjfjwa
Survey on Deep Learning-Based Marine Object Detection
2021
Journal of Advanced Transportation
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We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping ...
application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. ...
Acknowledgments is work was supported in part by the Fundamental Research Funds for the Central Universities, Grant nos. 3132021130 and 3132019400. ...
doi:10.1155/2021/5808206
fatcat:y3epygwit5efxnlhv4hp7uodqy
A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance
[article]
2022
arXiv
pre-print
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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. ...
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. ...
ACKNOWLEDGEMENT This research is supported by the Commonwealth of Australia as represented by the Defence Science and Technology Group of the Department of Defence. ...
arXiv:2207.12926v1
fatcat:fjcuijt2f5d63apgg67eiydofa
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets
[article]
2021
arXiv
pre-print
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At the end, we showed future directions with existing challenges of the field. In the future, OD methods and models can be analyzed for real-time object detection, tracking strategies. ...
This survey paper specially analyzed computer vision-based object detection challenges and solutions by different techniques. ...
[62] 3 Tiny object detection 2021 A survey and performance evaluation of deep learning methods for small object detection [81] 4 Deep learning 2015 An introduction to deep learning and applications ...
arXiv:2107.07927v1
fatcat:pgwxu5tnvzhj7ln3ccndmpilsi
Survey of Face Detection on Low-quality Images
[article]
2018
arXiv
pre-print
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Our results demonstrate that both hand-crafted and deep-learning based face detectors are not robust enough for low-quality images. ...
Face detection is a well-explored problem. Many challenges on face detectors like extreme pose, illumination, low resolution and small scales are studied in the previous work. ...
CONCLUSIONS In this paper, we made a survey on face detection algorithms, evaluated the representatives of them: Haar-like Adaboost cascade and HoG-SVM as traditional methods, and faster R-CNN and S 3 ...
arXiv:1804.07362v1
fatcat:mw2qljjif5drzlplmjvwlfvsyq
Going Deeper Into Face Detection: A Survey
[article]
2021
arXiv
pre-print
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In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural ...
Inspired by the rapid progress of deep learning in computer vision, many deep learning based frameworks have been proposed for face detection over the past few years, achieving significant improvements ...
ACKNOWLEDGMENTS The authors would like to thank Aleksei Stoliar for his comments and suggestions regarding this work. ...
arXiv:2103.14983v2
fatcat:3pdac7jpvzegdnz7qzqdrs3vx4
Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks
2019
ISPRS International Journal of Geo-Information
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object scales and a multi-level convolution matching network (M-CMN) for object detection based on fused feature maps, which combines several feature maps that enable small and densely packed manhole ...
Recently, deep learning models, especially deep convolutional neural networks (DCNNs), have proven to be effective at object detection. ...
The F1-score is the combined precision and recall metrics in a single measure for comprehensively evaluating the quality of an object detection method [4] . ...
doi:10.3390/ijgi8010049
fatcat:h2llpmgk55g2tkucxintpszx6e
Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review
2020
IEEE Access
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This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. ...
In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational ...
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. ...
doi:10.1109/access.2020.3021508
fatcat:guri46oiejhfzeitxuuprpmjka
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
[article]
2021
arXiv
pre-print
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The proposed taxonomy sub-categorises the use of deep learning approaches into supervised, semi-supervised and unsupervised learning, with a particular focus on object classification, detection, segmentation ...
Based on the current and future trends in deep learning, the paper finally presents a discussion and future directions for X-ray security imagery. ...
Although the use of transfer learning improves the performance of small X-ray datasets, the lack of large datasets limits contemporary deep model training. ...
arXiv:2001.01293v2
fatcat:qsb2zg33tbevldqljgycmxnhf4
A Survey on Approaches for Saliency Detection with Visual Attention
2018
MATEC Web of Conferences
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This article provides a survey of saliency detection with visual attention, which exploit visual cues of foreground salient areas, visual attention based on saliency map, and deep learning based saliency ...
Nowadays, a few approaches not only consider the difference between the foreground objects and the surrounding background areas, but also consider the saliency objects as the candidates for the center ...
DEEP LEARNING BASED SALIENCY DETECTION Recently, in the task of computer vision, deep convolutional neural networks (CNN) has reached the forefront of performance and has become a powerful method for extracting ...
doi:10.1051/matecconf/201823202007
fatcat:o3s4rb7gqzdq7jlemixvykagum
Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement learning and Q-learning
2020
Journal of Information Processing Systems
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Among these algorithms, the expansion of deep learning is rapidly changing. ...
Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication ...
He is a member of the IEEE, IEEE Computer Society, KIPS, and KMMS. ...
doi:10.3745/jips.02.0139
dblp:journals/jips/ParkP20d
fatcat:g4kjzhhxafeexn4hkdaqeknnre
Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance
2022
2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
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In addition, we provide a systematic analysis of the relationship between animal resolution and detection performance. ...
In this paper, we address the problem for the first time by combining deep object detectors with super-resolution techniques and altitude data. ...
For the public SAVMAP and AED datasets the setup proves highly effective, improving benchmarks beyond baselines and prior works.
2. 1 . 1 Deep Learning for Animal Detection Deep Object Detectors. ...
doi:10.1109/wacvw54805.2022.00057
fatcat:krqwt5aqdze3vckc6savgvhun4
Deep Learning – A first Meta-Survey of selected Reviews across Scientific Disciplines and their Research Impact
[article]
2020
arXiv
pre-print
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like object detection. ...
For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides over 11 000 results for the search term 'deep learning' in Q3 2020, and 90 are ...
Acknowledgements This work sees the funding of the Austrian Science Fund (FWF) KLI 678-B31:
Additional Information Competing financial interests: The authors declare no competing financial interests ...
arXiv:2011.08184v1
fatcat:7eofypvqordn7i4o7qmtoaaydi
A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving
2021
Electronics
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This paper aims to provide a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving. ...
We also summarize the benchmark datasets and evaluation criteria used in the research community and make a performance comparison of some of the latest works. ...
This paper presents a survey on research using deep learning-based approaches for scene understanding in autonomous driving, especially focusing on two main tasks: object detection and scene segmentation ...
doi:10.3390/electronics10040471
fatcat:gyloykg24nbqvlw4ujiiagoneq
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