Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4

Wang, Yongjie; Bai, Miaoyuan; Wang, Mingzhi; Zhao, Fengfeng; Guo, Jifeng

doi:10.1155/2022/5297605

Cited by 8 publications

(6 citation statements)

References 35 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Autoencoders, as an unsupervised learning method 39 , aim to reduce dimensionality and extract features by learning compressed representations of data. Their fundamental design includes an encoder, a bottleneck layer, and a decoder.…”

Section: Unsupervised Learning Feature Extraction Methodsmentioning

confidence: 99%

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis

Zhang,

Guo,

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.

show abstract

Section: Unsupervised Learning Feature Extraction Methodsmentioning

confidence: 99%

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis

Zhang,

Guo,

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The above methods study shallow GCNs. In the field of image classification, the CNN depth is increasing [22] to extract better and express the raw features and improve model performance. However, there is a shortage of research on deep-layer GCNs applied to ASD diagnosis.…”

Section: Multimodal Autism Spectrum Disorder Diagnosis Methods Based ...mentioning

confidence: 99%

Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN

Wang,

Guo,

Wang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

View full text Add to dashboard Cite

Multimodal data play an important role in the diagnosis of brain diseases. This study constructs a whole-brain functional connectivity network based on functional MRI data, uses non-imaging data with demographic information to complement the classification task for diagnosing subjects, and proposes a multimodal and across-site WL-DeepGCN-based method for classification to diagnose autism spectrum disorder (ASD). This method is used to resolve the existing problem that deep learning ASD identification cannot efficiently utilize multimodal data. In the WL-DeepGCN, a weight-learning network is used to represent the similarity of non-imaging data in the latent space, introducing a new approach for constructing population graph edge weights, and we find that it is beneficial and robust to define pairwise associations in the latent space rather than the input space. We propose a graph convolutional neural network residual connectivity approach to reduce the information loss due to convolution operations by introducing residual units to avoid gradient disappearance and gradient explosion. Furthermore, an EdgeDrop strategy makes the node connections sparser by randomly dropping edges in the raw graph, and its introduction can alleviate the overfitting and oversmoothing problems in the DeepGCN training process. We compare the WL-DeepGCN model with competitive models based on the same topics and nested 10-fold cross-validation show that our method achieves 77.27% accuracy and 0.83 AUC for ASD identification, bringing substantial performance gains.

show abstract

“…In recent years, machine learning methods, including neural networks and deep learning methods [12,13], have been utilized to diagnose ASD [14][15][16][17][18]. Heinsfeld et al (2018) [19] categorized 1,035 participants based on their ASD status, where 505 participants had ASD and 530 were healthy controls.…”

Section: Introductionmentioning

confidence: 99%

A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder

Wang,

Ma,

Wang

et al. 2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD.

show abstract

Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4

Cited by 8 publications

References 35 publications

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis

Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN

A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder

Contact Info

Product

Resources

About