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Going Deeper Into Face Detection: A Survey
[article]
2021
arXiv
pre-print
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With the breakthrough work in image classification using deep neural networks in 2012, there has been a huge paradigm shift in face detection. ...
Early approaches for face detection were mainly based on classifiers built on top of hand-crafted features extracted from local image regions, such as Haar Cascades and Histogram of Oriented Gradients. ...
ACKNOWLEDGMENTS The authors would like to thank Aleksei Stoliar for his comments and suggestions regarding this work. ...
arXiv:2103.14983v2
fatcat:3pdac7jpvzegdnz7qzqdrs3vx4
Image Enhancement Driven by Object Characteristics and Dense Feature Reuse Network for Ship Target Detection in Remote Sensing Imagery
2021
Remote Sensing
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Considering the characteristics of ship targets in RSIs, this study proposes a detection framework combining an image enhancement module with a dense feature reuse module: (1) drawing on the ideas of the ...
which can improve the efficiency of feature reuse in the network; (3) we introduced the receptive field expansion module to obtain a wider range of deep semantic information and enhance the ability to ...
The detection effect of multi-scale targets in a general single-scale network is not ideal, which often makes the detection accuracy for small target objects very low. ...
doi:10.3390/rs13071327
fatcat:phazoirsb5cfbajmwnbldx25lu
Learning Spatial Fusion for Single-Shot Object Detection
[article]
2019
arXiv
pre-print
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However, the inconsistency across different feature scales is a primary limitation for the single-shot detectors based on feature pyramid. ...
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection. ...
The Single Shot Detector (SSD) [25] is one of the first attempts to generate convolutional pyramidal feature representations for object detection. ...
arXiv:1911.09516v2
fatcat:wznxuly23vfp3lxrvzqvettyze
An FPGA-Accelerated Design for Deep Learning Pedestrian Detection in Self-Driving Vehicles
[article]
2018
arXiv
pre-print
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This project proposes two contributions to address this problem, by using a deep neural network used for object detection, called Single Shot Multi-Box Detector (SSD). ...
Currently, a special topology of deep neural networks called Fused Deep Neural Network (F-DNN) is considered to be the state of the art in pedestrian detection, as it has the lowest miss rate, yet it is ...
Haitham Akkary for guiding through this project, and for their never-ending advice and motivation. ...
arXiv:1809.05879v1
fatcat:vv5dndsfajbelivzrkekfjgncq
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
[article]
2022
arXiv
pre-print
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For the sake of avoiding conceptual confusion, we first elaborate and compare a set of similar concepts including few-shot learning, transfer learning, and meta-learning. ...
Furthermore, we propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge in accordance with the challenges of FSL. ...
Few-shot Object Detection Few-Shot Object Detection (FSOD) is the task of detecting rare objects from several samples. ...
arXiv:2205.06743v2
fatcat:xmxht2ileja53o2o5b4vrw32ey
Efficient Object Detection Framework and Hardware Architecture for Remote Sensing Images
2019
Remote Sensing
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To address these issues, we propose an efficient contextbased feature fusion single shot multibox detector (CBFFSSD) framework, using lightweight MobileNet as the backbone network to reduce parameters ...
Object detection in remote sensing images on a satellite or aircraft has important economic and military significance and is full of challenges. ...
for multi-scale object detection. ...
doi:10.3390/rs11202376
fatcat:6ubha7ol5bcx7mgpn3mnt3zawy
An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images
2021
Energies
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To improve the accuracy of insulator fault detection, SPP-networks and a multi-scale prediction network are employed for the improved YOLOv3 model. ...
Secondly, an improved YOLOv3 model is proposed to reuse features and prevent feature loss. ...
Acknowledgments: The authors wish to thank the editor and reviewers for their suggestions and thank Yiquan Wu for his guidance.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/en14144365
fatcat:k7563p72ebaw7liosncwmmn7ry
Few-Shot Object Detection with Fully Cross-Transformer
[article]
2022
arXiv
pre-print
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Metric-learning based methods have been demonstrated to be effective for this task using a two-branch based siamese network, and calculate the similarity between image regions and few-shot examples for ...
Our model can improve the few-shot similarity learning between the two branches by introducing the multi-level interactions. ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation therein. ...
arXiv:2203.15021v1
fatcat:bzfidqn6xfhnvp2nkusj6axks4
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
[article]
2020
arXiv
pre-print
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ScaleNAS solves multiple tasks at a time by searching multi-scale feature aggregation. ...
Scale variance among different sizes of body parts and objects is a challenging problem for visual recognition tasks. ...
One-shot NAS aims at searching a large neural architecture and sharing weights to different sub-networks [26, 4, 21, 6, 13] . ...
arXiv:2011.14584v1
fatcat:epf25rte2bb7laqqtf2t6mevtq
TinaFace: Strong but Simple Baseline for Face Detection
[article]
2021
arXiv
pre-print
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In this paper, we point out that there is no gap between face detection and generic object detection. Then we provide a strong but simple baseline method to deal with face detection named TinaFace. ...
On the hard test set of the most popular and challenging face detection benchmark WIDER FACE , with single-model and single-scale, our TinaFace achieves 92.1% average precision (AP), which exceeds most ...
“A unified multi-scale deep convo- [15] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hin-
lutional neural network for fast object detection”. In: ton. ...
arXiv:2011.13183v3
fatcat:5vqpkodpgzbpplj5jyog3pr6nu
FASSD: A Feature Fusion and Spatial Attention-Based Single Shot Detector for Small Object Detection
2020
Electronics
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We propose a feature fusion and spatial attention-based single shot detector (FASSD) for small object detection. ...
We fuse high-level semantic information into shallow layers to generate discriminative feature representations for small objects. ...
The same architecture is adopted by deconvolutional single shot detector (DSSD) [5] , single shot refinement neural network (RefineDet) [10] , and multi-level feature pyramid network based single shot ...
doi:10.3390/electronics9091536
doaj:c25675b12a0a405e9da70f04a58cefd4
fatcat:6ua5w6btbva7xilcg3fylfrtue
Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images
2020
IEEE Access
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Noting the shortcomings of current methods in detecting small objects in image-based remote sensing applications, in this paper, we propose a novel implementation of single shot multibox detector (SSD) ...
We call this algorithm dilated convolution and feature fusion single shot multibox detector (DFSSD). ...
The feature pyramid network (FPN) network uses the characteristics of multi-scale feature map to detect small objects with low-level and high-resolution feature maps and large objects with high-level and ...
doi:10.1109/access.2020.2991439
fatcat:5w7dpzko7rhhhgrdreanaqnyj4
Detecting Small Objects in Thermal Images Using Single-Shot Detector
2021
Automatic control and computer sciences
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2021 pre-print arXiv:2108.11101v1 fatcat:zbi4xm5rv5b2zlontrjx6itw4i
In this paper, we proposed DDSSD (Dilation and Deconvolution Single Shot Multibox Detector), an enhanced SSD with a novel feature fusion module which can improve the performance over SSD for small object ...
SSD (Single Shot Multibox Detector) is one of the most successful object detectors for its high accuracy and fast speed. ...
The original SSD detects objects by features from multi-scale layers directly, regarding these features in different levels as the same level. ...
doi:10.3103/s0146411621020097
fatcat:dvowomwfbzcbpcsdxfuch53bgq
Context-Aware Single-Shot Detector
[article]
2018
arXiv
pre-print
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In this paper, we present CSSD--a shorthand for context-aware single-shot multibox object detector. CSSD is built on top of SSD, with additional layers modeling multi-scale contexts. ...
The empirical results further strengthen our conclusion that SSD coupled with context layers achieves better detection results especially for small objects (+3.2% AP_@0.5 on MS-COCO compared to the newest ...
[9] proposed an object detection framework based on deep neural networks named R-CNN, which performs a forward pass for every object proposal and then classifies. ...
arXiv:1707.08682v2
fatcat:jmjjhggirnc4bkqy3qlsandohu
Water surface object detection using panoramic vision based on improved single-shot multibox detector
2021
EURASIP Journal on Advances in Signal Processing
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We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. ...
AbstractIn view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot ...
a deep neural network. ...
doi:10.1186/s13634-021-00831-6
fatcat:ti3xb6q24rgixmfdu2afbbvphi
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