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MR-CNN: A Multi-scale Region-based Convolutional Neural Network for Small Traffic Sign Recognition
2019
IEEE Access
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In this paper, we propose the multiscale region-based convolutional neural network (MR-CNN). ...
Small traffic sign recognition is a challenging problem in computer vision, and its accuracy is important to the safety of intelligent transportation systems (ITS). ...
FIGURE 1 . 1 The architecture of our proposed multi-scale region-based convolutional neural network (MR-CNN). ...
doi:10.1109/access.2019.2913882
fatcat:xi6d6uog2ngnpijmzqe52bm4le
Dense-RefineDet for Traffic Sign Detection and Classification
2020
Sensors
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Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. ...
Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs ...
[5] proposed MR-CNN, with contextual regions selected based on concatenated features in the MR-CNN to provide additional information for small objects. Noh et al. ...
doi:10.3390/s20226570
pmid:33213025
pmcid:PMC7698555
fatcat:g5qmplbq5zh6pgufrdrgboemcq
A lightweight model for multi-traffic object detection based on deep learning under complex traffic conditions
2021
Journal of Applied Science and Engineering
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The paper presents how to achieve precise results in multi-traffic object detection while minimizing the model size. A deep learning network YOLOv5s-Ghost-SE-DW is proposed based on the YOLOv5s. ...
The proposed network can detect all traffic objects including traffic signs and lights. First, the convolution layer is replaced by the Ghost module to reduce the parameter and model size. ...
MR-CNN uses a more accurate Region Proposal Network to transform the region before inputting, pays more attention to its context information and completes better screening and recommendation of the region ...
doi:10.6180/jase.202206_25(3).0019
doaj:457599980d104da9a852c81d3e6f55d5
fatcat:cr4ozugtdrbdhkvnafbu7ocwuq
Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features
2020
IEEE Access
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In this paper, we regard traffic sign detection as a region classification problem and propose a two-stage CNN-based approach to solve it. ...
INDEX TERMS Traffic sign detection, multi-scale, center-point estimation, local features. ...
MR-CNN [16] uses the multi-scale features and the surrounding context information of the candidate objects to detect traffic signs, but it ignores the important local features. ...
doi:10.1109/access.2020.2991195
fatcat:bgjdr4v6qzhwpla24wgkgermzq
Focus First: Coarse-to-Fine Traffic Sign Detection With Stepwise Learning
2020
IEEE Access
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[33] proposed a multi-scale region-based convolutional neural network (MR-CNN) for small traffic sign detection. ...
[7] constructed a multi-column cascading convolutional neural network, which further refreshed the recognition effect of traffic signs. Jin et al. ...
doi:10.1109/access.2020.3024583
fatcat:uxasq5ufhvbjrmh7ijfb2aj6zy
FAMN: Feature Aggregation Multipath Network for Small Traffic Sign Detection
2019
IEEE Access
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For example, different speed-limit traffic signs have differences solely from the speed numbers. ...
INDEX TERMS Traffic sign detection, small object detection, fine-grained classification. ...
It outperforms MR-CNN [17] by 3.2%, 2.2%, and 4.3%, for small, medium, and large size groups, respectively, and by 3.2% on average. ...
doi:10.1109/access.2019.2959015
fatcat:amlp5nawkzaevevnjged66s2hi
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets
[article]
2021
arXiv
pre-print
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., convolutions and convolutional neural networks (CNN), pooling operations with trending types. ...
In part 2) we mainly focused on tiny object detection techniques (multi-scale feature learning, Data augmentation, Training strategy (TS), Context-based detection, GAN-based detection). ...
multi-scale deep convolutional neural network for fast object detection MSCNN [152] A MultiPath network for object detection MPNet [153] Gated bi-directional CNN for object detection GBDNet [154] Contextual ...
arXiv:2107.07927v1
fatcat:pgwxu5tnvzhj7ln3ccndmpilsi
Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review
2020
IEEE Access
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(e.g., StuffNet, HyperNet), modified classification network (e.g., NOC), multi-region and multi-scale feature extraction (e.g., MR-CNN). ...
Real-time accurate traffic sign recognition helps drive by acquiring temporal and spatial information of the potential sign. ...
doi:10.1109/access.2020.3021508
fatcat:guri46oiejhfzeitxuuprpmjka
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
[article]
2016
arXiv
pre-print
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In this paper, we propose a novel computational saliency model, i.e., deep spatial contextual long-term recurrent convolutional network (DSCLRCN) to predict where people looks in natural scenes. ...
Moreover, we also integrate scene context modulation in DSLSTM for saliency inference, leading to a novel deep spatial contextual LSTM (DSCLSTM) model. ...
., traffic signs in street views as shown in (c)) or some exceptional objects (e.g., a bed in a forest as shown in (d)). ...
arXiv:1610.01708v1
fatcat:lokren3j3nb67axandywdt4b54
Visual saliency prediction based on deep learning
2020
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In Chapter 4, a novel deep learning model based on a Fully Convolutional Network (FCN) architecture is proposed. ...
The outcomes that have been achieved by neural network methods in a variety of tasks have highlighted their ability to predict visual saliency. ...
Therefore, based on the advantages of CNNs, we can use small and high receptive fields in down-sampling (e.g., multi-convolution layers, such as in the VGG-16 network) to create feature maps. ...
doi:10.48336/bzj8-n176
fatcat:gbladtscczhhnezlp4lxvnwf2u