Implementing a Real-Time, YOLOv5 based Social Distancing Measuring System for Covid-19

• URL: http://arxiv.org/abs/2204.03350v1
• Date: Thu, 7 Apr 2022 10:42:30 GMT
• Title: Implementing a Real-Time, YOLOv5 based Social Distancing Measuring System for Covid-19
• Authors: Narayana Darapaneni, Shrawan Kumar, Selvarangan Krishnan, Hemalatha K, Arunkumar Rajagopal, Nagendra, and Anwesh Reddy Paduri
• Abstract summary: We have developed a custom defined model YOLOv5 modified CSP (Cross Stage Partial Network) We have assessed the performance on COCO and Visdrone dataset with and without transfer learning. Our findings show that the developed model successfully identifies the individual who violates the social distances.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The purpose of this work is, to provide a YOLOv5 deep learning-based social distance monitoring framework using an overhead view perspective. In addition, we have developed a custom defined model YOLOv5 modified CSP (Cross Stage Partial Network) and assessed the performance on COCO and Visdrone dataset with and without transfer learning. Our findings show that the developed model successfully identifies the individual who violates the social distances. The accuracy of 81.7% for the modified bottleneck CSP without transfer learning is observed on COCO dataset after training the model for 300 epochs whereas for the same epochs, the default YOLOv5 model is attaining 80.1% accuracy with transfer learning. This shows an improvement in accuracy by our modified bottleneck CSP model. For the Visdrone dataset, we are able to achieve an accuracy of upto 56.5% for certain classes and especially an accuracy of 40% for people and pedestrians with transfer learning using the default YOLOv5s model for 30 epochs. While the modified bottleneck CSP is able to perform slightly better than the default model with an accuracy score of upto 58.1% for certain classes and an accuracy of ~40.4% for people and pedestrians.

Related papers

• Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data [0.08388591755871733]
  Hematological disorders, which involve 1% of conditions and genetic diseases, present significant diagnostic challenges. Our approach evaluates various machine learning settings offering efficacy of various machine variety learning (ML) models. When data is available, the proposed solution is a solution for achieving higher accuracy for small and unique datasets.
  arXiv  Detail & Related papers  (2024-11-23T15:51:15Z)
• Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset [0.0]
  The study utilizes transfer learning to fine-tune pre-trained models on the dataset. YOLOv8 outperforms other models, achieving a training accuracy of 97.39% and a validation F1-score of 96.50%.
  arXiv  Detail & Related papers  (2024-07-10T15:03:00Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Developing a Resource-Constraint EdgeAI model for Surface Defect Detection [1.338174941551702]
  We propose a lightweight EdgeAI architecture modified from Xception for on-device training in a resource-constraint edge environment. We evaluate our model on a PCB defect detection task and compare its performance against existing lightweight models. Our method can be applied to other resource-constraint applications while maintaining significant performance.
  arXiv  Detail & Related papers  (2023-12-04T15:28:31Z)
• Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data [60.64535309016623]
  We build smooth-tail data where the long-tailed distribution of categories decays smoothly to correct the bias towards head classes. We fine-tune the class-agnostic modules of the pre-trained model on the head class dominant replay data. We train a unified model on the tail class dominant replay data while transferring knowledge from the head class expert model to ensure accurate detection of all categories.
  arXiv  Detail & Related papers  (2023-05-22T08:53:50Z)
• Real-Time Flying Object Detection with YOLOv8 [0.0]
  This paper presents a generalized model for real-time detection of flying objects. We also present a refined model that achieves state-of-the-art results for flying object detection.
  arXiv  Detail & Related papers  (2023-05-17T06:11:10Z)
• Revisiting Classifier: Transferring Vision-Language Models for Video Recognition [102.93524173258487]
  Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. In this study, we focus on transferring knowledge for video classification tasks. We utilize the well-pretrained language model to generate good semantic target for efficient transferring learning.
  arXiv  Detail & Related papers  (2022-07-04T10:00:47Z)
• Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking [98.5984733963713]
  We propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. We establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data.
  arXiv  Detail & Related papers  (2022-04-14T15:54:01Z)
• Self-Supervised Pre-Training for Transformer-Based Person Re-Identification [54.55281692768765]
  Transformer-based supervised pre-training achieves great performance in person re-identification (ReID) Due to the domain gap between ImageNet and ReID datasets, it usually needs a larger pre-training dataset to boost the performance. This work aims to mitigate the gap between the pre-training and ReID datasets from the perspective of data and model structure.
  arXiv  Detail & Related papers  (2021-11-23T18:59:08Z)
• Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing [0.0]
  This system was found to be very accurate and achieved a 94.5% accuracy of SOTA HRNet 256x192. Our model adopts an encoder-decoder architecture and is carefully downsized to improve its efficiency.
  arXiv  Detail & Related papers  (2021-11-08T01:44:46Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)

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.