Comparative Analysis of Restricted Boltzmann Machine Models for Image Classification

Dewi, Christine; Chen, Rung-Ching; Hendry, .; Hung, Hsiu-Te

doi:10.1007/978-3-030-42058-1_24

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…IoU calculates the overlap ratio between the boundary box of the prediction (pred) and ground truth (gt), as shown in Equation ( 11) [47] [48].…”

Section: B Training Resultsmentioning

confidence: 99%

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

Chen,

Dewi,

Zhuang

et al. 2023

IEEE Access

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In recent years, artificial intelligence has led to rapid development and application across various industries, which has prompted this. One of the significant developments is the improvement of transportation methods. Accidents involving vehicles frequently result in a high number of fatalities as well as economic damage. Road detection is one of the applications that can be used by self-driving cars. Traffic accidents happen, but artificial intelligence is used in many nations to construct smart cities and apps for selfdriving cars. Since public road sign datasets have been used in significant research for road sign identification and analysis, these datasets are particularly significant for training autonomous vehicles. This study records the roads of various cities in Taiwan through road driving. It manually collects traffic signs in Taiwan to create a data set of road signs in Taiwan in daytime environments as well as nighttime environments. Since there is currently no data set of road signs in Taiwan, this study is necessary to create a data set of road signs in Taiwan. The YOLO model is utilized in this work to design road signs in Taiwan for mark detection. The techniques of Contrast Stretching (CS), Histogram Equalization (HE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) are evaluated in a nighttime setting and compared to the original image captured at night. The experimental results show that the best model during the day is YOLO V4 (no flip), the test set mAP is 86.77%, the Precision is 82%, the Recall is 87%, the F1-score is 84%, and the IoU is 63.92%. At night, the CLAHE image method works best in the YOLOv5x model, with a mAP of 86.40%. And YOLOv5 can be used in mobile devices or embedded devices, so this study recommends using CLAHE's YOLOv5x as the best model at night and used to improve the effect of road sign detection at night.

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“…IoU calculates the overlap ratio between the boundary box of the prediction (pred) and ground truth (gt), as shown in Equation ( 11) [47] [48].…”

Section: B Training Resultsmentioning

confidence: 99%

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

Chen,

Dewi,

Zhuang

et al. 2023

IEEE Access

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“…RBM is an unsupervised model to obtain a building block of DNN for supervised downstream tasks such as classification, in which the visible nodes are projected to hidden units (i.e., latent vector) with a reduced dimensionality [ [28] , [29] , [30] ]. The RBM based models effectively handle brain images because of their capacity to simplify high dimensional, functional imaging data while maintaining functional information of the brain [ 14 , 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Autoencoder and restricted Boltzmann machine for transfer learning in functional magnetic resonance imaging task classification

Hwang

Lustig

Jung

et al. 2023

Heliyon

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“…Dewi et al [17] used the YOLO approach in conjunction with the Generative Adversarial Network (GAN) in order to identify musical instruments that were comparable to one another. YOLO is a strong region-based convolutional neural network (CNN) that is extremely fast.…”

Section: Identifing Similar Musical Instruments With Cnnmentioning

confidence: 99%

Recognizing Similar Musical Instruments with YOLO Models

Dewi

Chen

Christanto

2023

BDCC

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Researchers in the fields of machine learning and artificial intelligence have recently begun to focus their attention on object recognition. One of the biggest obstacles in image recognition through computer vision is the detection and identification of similar items. Identifying similar musical instruments can be approached as a classification problem, where the goal is to train a machine learning model to classify instruments based on their features and shape. Cellos, clarinets, erhus, guitars, saxophones, trumpets, French horns, harps, recorders, bassoons, and violins were all classified in this investigation. There are many different musical instruments that have the same size, shape, and sound. In addition, we were amazed by the simplicity with which humans can identify items that are very similar to one another, but this is a challenging task for computers. For this study, we used YOLOv7 to identify pairs of musical instruments that are most like one another. Next, we compared and evaluated the results from YOLOv7 with those from YOLOv5. Furthermore, the results of our tests allowed us to enhance the performance in terms of detecting similar musical instruments. Moreover, with an average accuracy of 86.7%, YOLOv7 outperformed previous approaches and other research results.

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Comparative Analysis of Restricted Boltzmann Machine Models for Image Classification

Cited by 9 publications

References 9 publications

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

Autoencoder and restricted Boltzmann machine for transfer learning in functional magnetic resonance imaging task classification

Recognizing Similar Musical Instruments with YOLO Models

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