YOLO based approach for Helmet and Seatbelt Detection

@article{1704928,
      author = {Sudeep Manohar, Preethi B V, Preethi T, Sameeksha B, Sharvani S},
      title = {YOLO based approach for Helmet and Seatbelt Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {2},
      pages = {66-71},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704928.pdf},
      abstract = {Increase in the number of transportation vehicles has led to heavy traffic loads and thus many transportation routes are created. Due to this people might lose patience which may lead to many accidents and traffic injuries. Hence rules are enforced upon people to wear helmets while riding bikes and seat belts while driving cars to minimize the chances on casualty in case of accidents. Many people do not follow the traffic rules and get involved in traffic rule violation. Since the monitoring of those rules are done manually by traffic police things might get difficult for the police to monitor each and every vehicle. Hence, a model has been proposed to detect whether helmet or seat belt is worn using the machine learning technique. In this paper, a survey has been done and found that helmet detection model could be built with the help of Convolutional Neural Network (CNN) and You Only Look Once (YOLO)v3. YOLOv2 can also be used which has to be trained with COCO datasets. For seat belt detection YOLOv5 method is used along with the combination of CNN and Support Vector Machine (SVM) models. The proposed model decreases the burden on traffic police and also helps in achieving efficiency.},
      keywords = {Helmet, Seat belt, YOLOv2, YOLOv3, YOLOv5, Convolutional Neural Network, SVM},
      month = {August},
  }