An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

Han, Kun‐Yeun; Peng, Junwen; Yang, Qiongqian; Tian, Wentao

doi:10.1109/access.2021.3091434

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…The Melspectrogram, on the other hand, is obtained by dividing the frequency spectrum into fixed frequency bands and calculating the amplitude for each band. This allows the frequency axis to be converted to a Mel scale and features based on the human auditory system to be extracted, and it is known that CNNs outperform other classification models in image classification tasks using MFCC and Mel spectrograms, as studied by Han et al [41]. In cognitive function classification tasks, both are widely used; however, which one is more accurate depends on the task, and both need to be validated.…”

Section: Spontaneous Conversation Testmentioning

confidence: 99%

Questionnaires for the Assessment of Cognitive Function Secondary to Intake Interviews in In-Hospital Work and Development and Evaluation of a Classification Model Using Acoustic Features

Igarashi

Umeda‐Kameyama

Kojima

et al. 2023

Sensors

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The number of people with dementia is increasing each year, and early detection allows for early intervention and treatment. Since conventional screening methods are time-consuming and expensive, a simple and inexpensive screening is expected. We created a standardized intake questionnaire with thirty questions in five categories and used machine learning to categorize older adults with moderate and mild dementia and mild cognitive impairment, based on speech patterns. To evaluate the feasibility of the developed interview items and the accuracy of the classification model based on acoustic features, 29 participants (7 males and 22 females) aged 72 to 91 years were recruited with the approval of the University of Tokyo Hospital. The MMSE results showed that 12 participants had moderate dementia with MMSE scores of 20 or less, 8 participants had mild dementia with MMSE scores between 21 and 23, and 9 participants had MCI with MMSE scores between 24 and 27. As a result, Mel-spectrogram generally outperformed MFCC in terms of accuracy, precision, recall, and F1-score in all classification tasks. The multi-classification using Mel-spectrogram achieved the highest accuracy of 0.932, while the binary classification of moderate dementia and MCI group using MFCC achieved the lowest accuracy of 0.502. The FDR was generally low for all classification tasks, indicating a low rate of false positives. However, the FNR was relatively high in some cases, indicating a higher rate of false negatives.

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Section: Spontaneous Conversation Testmentioning

confidence: 99%

Questionnaires for the Assessment of Cognitive Function Secondary to Intake Interviews in In-Hospital Work and Development and Evaluation of a Classification Model Using Acoustic Features

Igarashi

Umeda‐Kameyama

Kojima

et al. 2023

Sensors

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show abstract

“…In the last few years, due to the rapid advancement of computer technology, it has become possible to apply GPUs for massively parallel computing to train large deep neural networks [ 6 , 7 ]. Object detection based on deep learning has received more and more attention as a non-invasive method.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Helmet Detection Algorithm Using an Improved YOLOv4

Chen

Deng

Wang

et al. 2023

Sensors

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Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper proposes an improved YOLOv4 algorithm to achieve real-time and efficient safety helmet wearing detection. The improved YOLOv4 algorithm adopts a lightweight network PP-LCNet as the backbone network and uses deepwise separable convolution to decrease the model parameters. Besides, the coordinate attention mechanism module is embedded in the three output feature layers of the backbone network to enhance the feature information, and an improved feature fusion structure is designed to fuse the target information. In terms of the loss function, we use a new SIoU loss function that fuses directional information to increase detection precision. The experimental findings demonstrate that the improved YOLOv4 algorithm achieves an accuracy of 92.98%, a model size of 41.88 M, and a detection speed of 43.23 pictures/s. Compared with the original YOLOv4, the accuracy increases by 0.52%, the model size decreases by about 83%, and the detection speed increases by 88%. Compared with other existing methods, it performs better in terms of precision and speed.

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“…Thanks to the development of computer technology, applying GPU in large-scale parallel computing makes training large deep neural networks possible [17], [18]. In the field of object detection, a series of methods based on deep learning were developed, and convolution neural networks (CNNs) are most commonly used because of their superiority in high-level feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

Han

Zeng

2022

IEEE Access

Self Cite

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Wearing safety helmets can effectively protect workers safety on construction sites. However, workers often take off the helmets because of weak security-conscious and discomfort, then hidden dangers will be brought by this behaviour. Workers without safety helmets will suffer more injuries in accidents such as falling human body and vertical falling matter. Hence, detecting safety helmet wearing is a vital step of construction sites safety management and a safety helmet detector with high speed and accuracy is urgently needed. However, traditional manual monitor is labour intensive and methods of installing sensors on safety helmet are difficult to popularize. Therefore, this paper proposes a deep learning-based method to detect safety helmet wearing at a satisfactory accuracy with high detection speed. Our method chooses YOLO v5 as the baseline, then the fourth detection scale is added to predict more bounding boxes for small objects and the attention mechanism is adopted in the backbone of the network to construct more informative features for following concatenation operations. In order to overcome the defects caused by insufficient data, targeted data augmentation and transfer learning are used. Improvements caused by every modification are discussed in this paper. Finally, our model achieves 92.2% mean average precision, up 6.3% compared to the original algorithm, and it only takes 3.0 ms to detect an image at 640 × 640. These results demonstrate the robustness and feasibility of our model. Meanwhile, the size of our trained model is only 16.3 m, which means the model is easy to be deployed. At last, after obtaining a satisfactory model, a graphical user interface (GUI) is designed to make our algorithm more user-friendly.INDEX TERMS Safety helmet wearing detection, YOLO v5, four detection scales, attention mechanism, GUI design.

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An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

Cited by 7 publications

References 47 publications

Questionnaires for the Assessment of Cognitive Function Secondary to Intake Interviews in In-Hospital Work and Development and Evaluation of a Classification Model Using Acoustic Features

Questionnaires for the Assessment of Cognitive Function Secondary to Intake Interviews in In-Hospital Work and Development and Evaluation of a Classification Model Using Acoustic Features

Lightweight Helmet Detection Algorithm Using an Improved YOLOv4

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

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