Scaling up face masks detection with YOLO on a novel dataset

Kumar, Akhil; Kalia, Arvind; Verma, Kinshuk; Sharma, Akashdeep; Kaushal, Manisha

doi:10.1016/j.ijleo.2021.166744

Cited by 71 publications

(36 citation statements)

References 13 publications

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“…With hundreds of items obtained, all the searched journal papers [12], [22]- [51] are selected for review due to their detailed descriptions, experiments and discussions. Some conference papers are filtered out under the conditions: 1) not written in English; 2) without experiments especially lacking of quantitative results; 3) unclear expressions or disordered organization; 4) without visual detection results shown; 5) number of images in dataset is too small, e.g., ≤ 500.…”

Section: A the Stats Of Surveyed Literaturesmentioning

confidence: 99%

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Wang

Zheng

Chen

2022

IEEE Trans. Artif. Intell.

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Section: A the Stats Of Surveyed Literaturesmentioning

confidence: 99%

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Wang

Zheng

Chen

2022

IEEE Trans. Artif. Intell.

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“…The experimental results revealed that the combination of the original images with the synthetic images from LSGAN resulted in the best detection performance of 84.9% on YOLOv3 and 89.33% on YOLOv4. Kumar et al [5] proposed a novel face mask dataset due to the unavailability of appropriate datasets for face mask detection. The dataset was tested using YOLO to determine its effectiveness.…”

Section: Related Workmentioning

confidence: 99%

LONTAR_DETC: Dense and High Variance Balinese Character Detection Method in Lontar Manuscripts

2022

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This paper proposed LONTAR_DETC, a method to detect handwritten Balinese characters in Lontar manuscripts. LONTAR_DETC is a deep learning architecture based on YOLO. The detection of Balinese characters in Lontar manuscripts is challenging due to the characteristics of Balinese characters in Lontar manuscripts. Balinese characters in Lontar manuscripts are dense, overlapping, have high variance, contain noise, and classes of these characters are imbalanced. The proposed method consists of three steps, namely data generation, Lontar manuscript annotation, and Balinese character detection. The first step is data generation, in which synthetic images of original Lontar manuscript images are generated with enhanced image quality. The second step is data annotation to build a new Lontar manuscript dataset. As a result, we also propose the Handwritten Balinese Character of Lontar manuscript (HBCL_DETC) dataset, a novel Balinese character in Lontar manuscripts dataset. HBCL_DETC contains 600 images that consists of more than 100,000 Balinese characters annotated by experts. Finally, the third step is training the YOLOv4 detection model using the HBCL_DETC dataset. We created this dataset specifically for the task of detecting Balinese characters in Lontar manuscripts. To evaluate the reliability of the dataset, we experimented with three scenarios. In the first scenario, the detection model was trained using original images of Lontar manuscripts, in the second scenario the detection model was trained with the addition of augmented grayscale images, and in the third scenario the detection model was trained using HBCL_DETC. Based on the experimental results, LONTAR_DETC can detect Balinese characters with high detection rate with mAP of 99.55%.

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“…Penggunaan MobileNetV2, yang memang dirancang untuk diimplementasikan pada perangkat mobile, menghasilkan frame rate yang cukup baik, yaitu 15,71 frame per second (FPS). Penelitian [7] mengusulkan dataset baru untuk deteksi masker wajah dan melakukan pengujian dengan membandingkan beberapa varian algoritma YOLO dan mendapatkan hasil terbaik mean average precision (mAP) sebesar 71,69%. Penelitian [8] menggunakan gabungan antara jaringan ResNet-50 untuk ekstraksi ciri dan YOLO v2 untuk deteksi masker wajah.…”

Section: Penyebaran Awal Virus Ini Ditemukan DIunclassified

Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local Binary Patterns

Wihandika

2021

RESTI

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The COVID-19 pandemic is still ongoing until 2021 and is likely to continue until an uncertain time. This arises because the spread of the SARS-CoV-2 virus also continued to occur in the community. Of the five points in 5M that has been initiated by the government, the focus of this study is the use of face masks. In this study, an image-based automatic mask detection method using a classification approach is proposed. This method can be used in automated systems to increase public discipline in wearing masks to suppress the spread of the SARS-CoV-2 virus. The classes used in the classification are "with mask" and "without mask". The adjacent evaluation local binary patterns (AELBP) method, which is an extension of the local binary patterns (LBP) method, is used to extract the texture features of each image. Tests were carried out on 2,172 facial images of various sizes, facial accessories, and facial expressions. The test results using the AELBP method show that the accuracy and F-measure are 98.39% and 98.08%, respectively. This result is better than other methods which are also evaluated. In addition, testing of the AELBP method execution time shows that this method is feasible to use on real systems.

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Scaling up face masks detection with YOLO on a novel dataset

Cited by 71 publications

References 13 publications

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

LONTAR_DETC: Dense and High Variance Balinese Character Detection Method in Lontar Manuscripts

Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local Binary Patterns

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