Real-Time Mask Detection Based on SSD-MobileNetV2

• URL: http://arxiv.org/abs/2208.13333v1
• Date: Mon, 29 Aug 2022 01:59:22 GMT
• Title: Real-Time Mask Detection Based on SSD-MobileNetV2
• Authors: Chen Cheng
• Abstract summary: 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.
• Score: 2.538209532048867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: After the outbreak of COVID-19, mask detection, as the most convenient and effective means of prevention, plays a crucial role in epidemic prevention and control. An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff. However, by analyzing the existing mask detection approaches, we find that they are mostly resource-intensive and do not achieve a good balance between speed and accuracy. And there is no perfect face mask dataset at present. In this paper, we propose a new architecture for mask detection. Our system uses SSD as the mask locator and classifier, and further replaces VGG-16 with MobileNetV2 to extract the features of the image and reduce a lot of parameters. Therefore, our system can be deployed on embedded devices. Transfer learning methods are used to transfer pre-trained models from other domains to our model. Data enhancement methods in our system such as MixUp effectively prevent overfitting. It also effectively reduces the dependence on large-scale datasets. By doing experiments in practical scenarios, the results demonstrate that our system performed well in real-time mask detection.

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)
• Mask Propagation for Efficient Video Semantic Segmentation [63.09523058489429]
  Video Semantic baseline degradation (VSS) involves assigning a semantic label to each pixel in a video sequence. We propose an efficient mask propagation framework for VSS, called SSSS. Our framework reduces up to 4x FLOPs compared to the per-frame Mask2Former with only up to 2% mIoU on the Cityscapes validation set.
  arXiv  Detail & Related papers  (2023-10-29T09:55:28Z)
• A transfer learning approach with convolutional neural network for Face Mask Detection [0.30693357740321775]
  We propose a mask recognition system based on transfer learning and Inception v3 architecture. In addition to masked and unmasked faces, it can also detect cases of incorrect use of mask.
  arXiv  Detail & Related papers  (2023-10-29T07:38:33Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach [0.0]
  During the SARS-Cov-2 pandemic, mask-wearing became an effective tool to prevent spreading and contracting the virus. The ability to monitor the mask-wearing rate in the population would be useful for determining public health strategies against the virus. We present a two-step face mask detection approach consisting of two separate modules: 1) face detection and alignment and 2) face mask classification.
  arXiv  Detail & Related papers  (2021-12-30T12:32:33Z)
• Adaptive Shrink-Mask for Text Detection [91.34459257409104]
  Existing real-time text detectors reconstruct text contours by shrink-masks directly. The dependence on predicted shrink-masks leads to unstable detection results. Super-pixel Window (SPW) is designed to supervise the network.
  arXiv  Detail & Related papers  (2021-11-18T07:38:57Z)
• 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)
• Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone [35.26022029969275]
  Syndrome coronaviruses 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. We propose a detection system based on the mobile phone.
  arXiv  Detail & Related papers  (2020-10-09T08:49:52Z)

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.