Lightweight Real-Time Mask Wearing Detection Algorithm Based on YOLOv3

Wang, Tao; Wang, Xin; Shi, Wei; Han, Yiyuan; Dong, Jia-Wei

doi:10.1109/icacr53472.2021.9605173

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Through experimental evaluation, our model has outperformed other lightweight facemask detection models based on YOLO [20][21][22][23] (e.g., YOLOv3/v4-Tiny/v5). The model size is 1.92 M, and the mAP (mean average precision) value reaches 93.5%.…”

Section: Introductionmentioning

confidence: 95%

Lightweight DB-YOLO Facemask Intelligent Detection and Android Application Based on Bidirectional Weighted Feature Fusion

Qin,

Zeng,

Wang

et al. 2023

Electronics

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Conventional facemask detection algorithms face challenges of insufficient accuracy, large model size, and slow computation speed, limiting their deployment in real-world scenarios, especially on edge devices. Aiming at addressing these issues, we proposed a DB-YOLO facemask intelligent detection algorithm, which is a lightweight solution that leverages bidirectional weighted feature fusion. Our method is built on the YOLOv5 algorithm model, replacing the original YOLOv5 backbone network with the lightweight ShuffleNetv2 to reduce parameters and computational requirements. Additionally, we integrated BiFPN as the feature fusion layer, enhancing the model’s detection capability for objects of various scales. Furthermore, we employed a CARAFE lightweight upsampling factor to improve the model’s perception of details and small-sized objects and the EIOU loss function to expedite model convergence. We validated the effectiveness of our proposed method through experiments conducted on the Pascal VOC2007+2012 and Face_Mask datasets. Our experimental results demonstrate that the DB-YOLO model boasts a compact size of approximately 1.92 M. It achieves average precision values of 70.1% and 93.5% on the Pascal VOC2007+2012 and Face_Mask datasets, respectively, showcasing a 2.3% improvement in average precision compared to the original YOLOv5s. Furthermore, the model’s size is reduced by 85.8%. We also successfully deployed the model on Android devices using the NCNN framework, achieving a detection speed of up to 33 frames per second. Compared to lightweight algorithm models like YOLOv5n, YOLOv4-Tiny, and YOLOv3-Tiny, DB-YOLO not only reduces the model’s size but also effectively improves detection accuracy, exhibiting excellent practicality and promotional value on edge devices.

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Section: Introductionmentioning

confidence: 95%

Lightweight DB-YOLO Facemask Intelligent Detection and Android Application Based on Bidirectional Weighted Feature Fusion

Qin,

Zeng,

Wang

et al. 2023

Electronics

Self Cite

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Real-time Mask Detection with YOLOv3 and Ensemble Residual Networks

Dr. J. Anvar Shathik,

Dr. Krishna Prasad K

2024

Int J Sci Res Sci & Technol

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The proposed system introduced an advanced face mask detections framework leveraging Adaptively YOLOv3 with Hybrid Atrous Convolution-depend Face Detector, optimized through an Ensembles residual Learning Network. This approach aims to enhance the accuracy level and efficacy of face mask detection in real-time situation. By integrating of the adaptive capability of YOLOv3 on the spatial benefits of atrous convolution defined, the system effectively captured varying of facial features and masks placements. The ensemble residual learning further refined detection by amalgamating of multiple learning models, by reducing false positives values and negatives. This hybrid model is meticulous optimized to maintaining of higher detection precision rate and recall, for ensuring robust performance even over diverse and complex conditions. The system’s efficacy will underscored by reduces the inference time and enhanced processing throughput rate, by making it highly suitable for large-scale deployment on public safety applications. By rigorously testing phase, the proposed system demonstrated significant improvements in detection accuracy rate, inference speed, and overall reliability, positioning it will be a pivotal tool in enforcing healthy safety protocols on various public and private settings.

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Lightweight Real-Time Mask Wearing Detection Algorithm Based on YOLOv3

Cited by 2 publications

References 14 publications

Lightweight DB-YOLO Facemask Intelligent Detection and Android Application Based on Bidirectional Weighted Feature Fusion

Lightweight DB-YOLO Facemask Intelligent Detection and Android Application Based on Bidirectional Weighted Feature Fusion

Real-time Mask Detection with YOLOv3 and Ensemble Residual Networks

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