A robust context attention network for human hand detection

Xie, Zhihuai; Wang, Shaojie; Zhao, Wentian; Guo, Zhenhua

doi:10.1016/j.eswa.2022.118132

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…As is the case in skin detection, deep-learning methods are used for hand segmentation to achieve a cutting-edge performance. Current state-of-the-art approaches for human hand detection [ 69 ] have achieved great success by making good use of multiscale and contextual information, but still remain unsatisfactory for hand segmentation, especially in complex scenarios. In this context, deep approaches have faced some difficulties, such as the clutter in the background that hinders the reliable detection of hand gestures in real-world environments.…”

Section: Methods For Skin Detectionmentioning

confidence: 99%

“…Among the several recent studies focused on hand segmentation, we cite the following: Refined U-net [ 19 ]: The authors proposed a refinement of U-net that performs with a few parameters and increases the inference speed, while achieving high accuracy during the hand-segmentation process. CA-FPN [ 69 ] stands for Context Attention Feature Pyramid Network and is a model designed for human hand detection. In this method, a novel Context Attention Module (CAM) is inserted into the feature pyramid networks.…”

Section: Methods For Skin Detectionmentioning

confidence: 99%

“…CA-FPN [ 69 ] stands for Context Attention Feature Pyramid Network and is a model designed for human hand detection. In this method, a novel Context Attention Module (CAM) is inserted into the feature pyramid networks.…”

Section: Methods For Skin Detectionmentioning

confidence: 99%

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A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

et al. 2023

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Skin detection involves identifying skin and non-skin areas in a digital image and is commonly used in various applications, such as analyzing hand gestures, tracking body parts, and facial recognition. The process of distinguishing between skin and non-skin regions in a digital image is widely used in a variety of applications, ranging from hand-gesture analysis to body-part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches, using several different datasets. The main contributions of this work are (i) a comprehensive review of the literature on approaches to skin-color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; and (iii) a testing protocol for evaluating and comparing different skin-detection approaches. Moreover, we propose an ensemble of convolutional neural networks and transformers that obtains a state-of-the-art performance.

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Section: Methods For Skin Detectionmentioning

confidence: 99%

Section: Methods For Skin Detectionmentioning

confidence: 99%

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A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

et al. 2023

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“…Deformable convolution 16 is a convolutional technique that can highly efficiently obtain contextual information. In previous works, 1 , 17 , 18 it has been observed that extracting contextual information of objects can help models achieve better robustness when faced with occlusion and similar interferences. However, traditional methods, such as dilated convolutions, 19 heavily rely on handcraft selecting dilation coefficients, and increasing the number of dilated convolutions to obtain more contextual information leads to higher computational and memory requirements.…”

Section: Deformable Convolutionmentioning

confidence: 99%

2DDSRU-MobileNet: an end-to-end cloud-noise-robust lightweight convolution neural network

Zhang,

Li,

Wang

2024

J. Appl. Rem. Sens.

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To solve the problem of thin-cloud interference of remote sensing (RS) images under hardware-constrained environments, we present an end-to-end cloudnoise-robust lightweight convolution neural network model, 2DDSRU-MobileNet, based on MobileNetV3-small. We first propose a denoised module, which notably improves the robustness of lightweight convolution neural network under a thincloud environment. Then using the idea of a deep residual shrinkage network, we construct a two-dimensional deep shrinkage residual unit (2DDSRU). Then we introduce MobileNetV3-small as a background model to solve the problem of RS obstructed by thin cloud; this enables our method to obtain a higher efficiency compared with other methods. Moreover, deformable convolution supplants conventional dilated convolution, leading to agile capturing of contextual information in the thin-cloud image. Finally, the model's efficiency and accuracy are evaluated using the RSSCN-cloud and UCM-cloud datasets. The experimental results demonstrate the cloud-noise-robust performance of our model in the thin-cloud environment. Under a thin-cloud environment, the proposed 2DDSRU-MobileNet model surpasses classic models, such as AlexNet, VGG16, ResNet50, MobileNet series, ShuffleNet-v1, Efficientnet-b3, GhostNetV2, and MobileViT, in terms of runtime computational complexity, network parameters, and classification accuracy.

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“…Unlike previous datasets, AeBAD exhibits significant differences such as varying image sizes, random positions, diverse lighting intensities, and random background colour changes. Moreover, it is equally critical to consider both local features, which capture specific patterns within anomaly regions (Kelishadrokhi et al, 2023;Xie et al, 2024), and global features that provide an overarching view of the entire image. Both aspects play a key role in the detection process, as combining these complementary perspectives can significantly boost the performance of anomaly detection models (Covert et al, 2020).…”

mentioning

confidence: 99%

Robust anomaly detection in industrial images by blending global–local features

Pei,

Liu,

Xia

2024

Expert Systems

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Industrial image anomaly detection achieves automated detection and localization of defects or abnormal regions in images through image processing and deep learning techniques. Currently, utilizing the approach of reverse knowledge distillation has yielded favourable outcomes. However, it is still a challenge in terms of the feature extraction capability of the image and the robustness of the decoding of the student network. This study first addresses the issue that the teacher network has not been able to extract global information more effectively. To acquire more global information, a vision transformer network is introduced to enhance the model's global information extraction capability, obtaining better features to further assist the student network in decoding. Second, for anomalous samples, to address the low similarity between features extracted by the teacher network and features restored by the student network, Gaussian noise is introduced. This further increases the probability that the features decoded by the student model match normal sample features, enhancing the robustness of the student model. Extensive experiments were conducted on industrial image datasets AeBAD, MvtecAD, and BTAD. In the AeBAD dataset, under the PRO performance metric, the result is 89.83%, achieving state‐of‐the‐art performance. Under the AUROC performance metric, it reaches 83.35%. Similarly, good results were achieved on the MvtecAD and BTAD datasets. The proposed method's effectiveness and performance advantages were validated across multiple industrial datasets, providing a valuable reference for the application of industrial image anomaly detection methods.

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A robust context attention network for human hand detection

Cited by 12 publications

References 13 publications

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

2DDSRU-MobileNet: an end-to-end cloud-noise-robust lightweight convolution neural network

Robust anomaly detection in industrial images by blending global–local features

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