A New Method on Mask-Wearing Detection for Natural Population Based on Improved YOLOv4

• URL: http://arxiv.org/abs/2208.11353v2
• Date: Mon, 09 Dec 2024 14:24:52 GMT
• Title: A New Method on Mask-Wearing Detection for Natural Population Based on Improved YOLOv4
• Authors: Xuecheng Wu, Mengmeng Tian, Lanhang Zhai,
• Abstract summary: This paper proposes a new mask-wearing detection method based on improved YOLOv4. We add the Coordinate Attention Module to the backbone to coordinate feature fusion and representation. Thirdly, we adaptively deploy the K-means clustering algorithm to make the nine anchor boxes more suitable for our NPMD dataset.
• Score: 0.3277163122167434
• License:
• Abstract: Recently, the domestic COVID-19 epidemic situation is serious, but in public places, some people do not wear masks or wear masks incorrectly, which requires the relevant staff to instantly remind and supervise them to wear masks correctly. However, in the face of such an important and complicated work, it is very necessary to carry out automated mask-wearing detection in public places. This paper proposes a new mask-wearing detection method based on improved YOLOv4. Specifically, firstly, we add the Coordinate Attention Module to the backbone to coordinate feature fusion and representation. Secondly, we conduct a series of network structural improvements to enhance the model performance and robustness. Thirdly, we adaptively deploy the K-means clustering algorithm to make the nine anchor boxes more suitable for our NPMD dataset. The experiments show that the improved YOLOv4 performs better, exceeding the baseline by 4.06\% AP with a comparable speed of 64.37 FPS.

Related papers

• ColorMAE: Exploring data-independent masking strategies in Masked AutoEncoders [53.3185750528969]
  Masked AutoEncoders (MAE) have emerged as a robust self-supervised framework. We introduce a data-independent method, termed ColorMAE, which generates different binary mask patterns by filtering random noise. We demonstrate our strategy's superiority in downstream tasks compared to random masking.
  arXiv  Detail & Related papers  (2024-07-17T22:04:00Z)
• Cut and Learn for Unsupervised Object Detection and Instance Segmentation [65.43627672225624]
  Cut-and-LEaRn (CutLER) is a simple approach for training unsupervised object detection and segmentation models. CutLER is a zero-shot unsupervised detector and improves detection performance AP50 by over 2.7 times on 11 benchmarks.
  arXiv  Detail & Related papers  (2023-01-26T18:57:13Z)
• Real-Time Mask Detection Based on SSD-MobileNetV2 [2.538209532048867]
  An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff. Existing mask detection approaches are resource-intensive and do not achieve a good balance between speed and accuracy. In this paper, we propose a new architecture for mask detection.
  arXiv  Detail & Related papers  (2022-08-29T01:59:22Z)
• An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection [3.3398969693904723]
  We propose an improved lightweight face mask detector based on YOLOv5. It achieves a mean average precision of 95.2%, which is 4.4% higher than the baseline and is also more accurate compared with other existing models.
  arXiv  Detail & Related papers  (2022-03-30T17:41:21Z)
• Should You Mask 15% in Masked Language Modeling? [86.91486000124156]
  Masked language models conventionally use a masking rate of 15%. We find that masking up to 40% of input tokens can outperform the 15% baseline.
  arXiv  Detail & Related papers  (2022-02-16T11:42:34Z)
• Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning [23.062034116854875]
  In the absence of vaccines or medicines to stop COVID-19, one of the effective methods to slow the spread of the coronavirus is to wear a face mask. To mandate the use of face masks or coverings in public areas, additional human resources are required, which is tedious and attention-intensive. We propose a face mask detection framework that uses the context attention module to enable the effective attention of the feed-forward convolution neural network.
  arXiv  Detail & Related papers  (2021-10-01T16:44:06Z)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)
• Application of Yolo on Mask Detection Task [1.941730292017383]
  Strict mask-wearing policies have been met with not only public sensation but also practical difficulty. Existing technology to help automate mask checking uses deep learning models on real-time surveillance camera footages. Our research proposes a new approach to mask detection by replacing Mask-R-CNN with a more efficient model "YOLO"
  arXiv  Detail & Related papers  (2021-02-10T12:34:47Z)
• BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices [63.56630165340053]
  Face masks offer an effective solution in healthcare for bi-directional protection against air-borne diseases. CNNs offer an excellent solution for face recognition and classification of correct mask wearing and positioning. CNNs can be used at entrances to corporate buildings, airports, shopping areas, and other indoor locations, to mitigate the spread of the virus.
  arXiv  Detail & Related papers  (2021-02-06T00:14:06Z)
• RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic [1.2691047660244335]
  One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. We proposed RetinaFaceMask, the first high-performance single stage face mask detector.
  arXiv  Detail & Related papers  (2020-05-08T10:45:16Z)
• Ternary Feature Masks: zero-forgetting for task-incremental learning [68.34518408920661]
  We propose an approach without any forgetting to continual learning for the task-aware regime. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.
  arXiv  Detail & Related papers  (2020-01-23T18:08:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.