CNN-based intelligent safety surveillance in green IoT applications

Safety surveillance is considered one of the most important factors in many constructing industries for green internet of things (IoT) applications. However, traditional safety monitoring methods require a lot of labor source. In this paper, we propose intelligent safety surveillance (ISS) method using a convolutional neural network (CNN), which is an auto-supervised method to detect workers whether or not wearing helmets. First, to train the CNN-based ISS model, the labeled datasets mainly come from two aspects: 1) our labeled datasets with the full labeled on both helmet and pedestrian; 2) public labeled datasets with the parts labeled either on the helmet or pedestrian. To fully take advantage of all datasets, we redesign CNN structure of network and loss functions based on YOLOv3. Then, we test our proposed ISS method based on the specific detection evaluation metrics. Finally, experimental results are given to show that our proposed ISS method enables the model to fully learn the labeled information from all datasets. When the threshold of intersection over union (IoU) between the predicted box and ground truth is set to 0.5, the average precision of pedestrians and helmets can reach 0.864 and 0.891, respectively.