Comparison Between Faster R-CNN and CNN in Recognizing Weaving Patterns

Rizki, Yoze; Taufiq, Reny Medikawati; Mukhtar, Harun; Wenando, Febby Apri; Amien, Januar Al

doi:10.1109/icimcis51567.2020.9354324

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Moreover, investigation on the detection of Malay woven fabrics utilized the faster R-CNN method. This study did not optimize Faster R-CNN, so this method's performance was only 82.14% [28].…”

Section: Introductionmentioning

confidence: 98%

Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout

Ermatita,

Noprisson,

Abdiansah

2024

Bulletin EEI

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A good way to spread knowledge about Palembang songket woven cloth patterns is to use information technology, especially artificial intelligence technology. This study's main goal is to develop a ResNet model with dropout regularization methods and find out how dropout regularization affects the ResNet model for detecting Palembang songket fabric motif with more data. Data was collected in places like tujuh saudara songket, Zainal songket, songket PaSH, AMS songket, and batik, Ernawati songket, Nabilah collections, Ilham songket, and Marissa songket. We used eight class of data for this research. A dataset of 7,680 data for training, 960 data for validation, and 960 data for testing is a dataset that has been prepared to be implemented in experiments. In the final results, the experimental results for DResNet demonstrated that accuracy at the training stage was 92.16%, accuracy at the validation stage was 78.60%, and accuracy at the submission stage was 80.3%. The experimental results also show that dropouts are able to increase the accuracy of the ResNet model by adding +1.10% accuracy in the training process, adding +1.80% accuracy in the validation process, and adding +0.40% accuracy in the testing process.

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Section: Introductionmentioning

confidence: 98%

Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout

Ermatita,

Noprisson,

Abdiansah

2024

Bulletin EEI

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“…Faster R-CNN and some other convolutional neural networks were considered for the recognition of traditional handicraft weaving patterns. The results of the study showed that the accuracy of Faster R-CNN reached 82.14% [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

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