# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
###### Abstract  
To reduce the risk of head trauma to workers working in high-risk workplaces such as construction sites, we designed a new automated lightweight end-to-end convolutional neural network to identify whether all people on a construction site are wearing helmets. Firstly, we used GhostNet as the backbone feature extraction network to take advantage of its low running cost and make the model lighter overall while ensuring efficient automatic feature extraction. Secondly, we designed a multi-scale segmentation and feature fusion network (MSFFN) in the feature-processing stage to improve the algorithm's robustness in detecting objects at different scales. In contrast, the design of the feature fusion network can enrich the diversity of helmet features and improve the accuracy of helmet detection when distance changes, viewpoint changes, and occlusion phenomena occur. Thirdly, we proposed an improved version of the attention mechanism, the lightweight residual convolutional attention network version 2 (LRCA-Netv2). The main idea of the improvement is implemented around the spatial dimension by fusing the combined features along with the horizontal and vertical directions and then weighting them separately. Such an operation allows the establishment of dependencies between the more distant features with improved accuracy compared to the original LRCA-Net. Finally, when tested on the dataset, the proposed lightweight helmet-wearing detection network has a mAP and FPS of 93.5% and 42.  
object detection; attention mechanism; lightweight network; helmet-wearing detection; computer vision; automated detection +
Footnote †: journal: Medical  
In recent years, many researchers have devoted their efforts to detecting workers in the field. Different researchers have tried many traditional techniques to detect the location and activity of workers. Most studies have used traditional vision-based computer vision and machine learning techniques, and the histogram of oriented gradients (HoG), background subtraction, and color segmentation, to detect the helmets of workers [2; 3; 4; 5; 6]. Most of these methods are computationally expensive, and the accuracy is low. With the development of deep learning, using convolutional neural networks (CNN) to locate and monitor workers has become very popular. Compared to traditional detection, CNN has improved dramatically in terms of accuracy. Many advanced algorithms such as SSD [7], YOLO [8], Faster-RCNN [9], and Efficient-Net [10] can detect features very accurately, and these methods can easily detect objects in real-time video streams. CNN and deep learning methods have several advantages over traditional methods. First, they can detect static and moving objects beyond traditional detection methods that rely on object movement. Secondly, they can detect workers in different poses, while traditional methods sometimes cannot detect objects when the workers' poses change. However, most of the existing helmet detection algorithms are not end-to-end; these methods first detect workers and then determine the presence of helmets within the prediction frame. Such detection has some logical problems. For example, the helmet will be certified as a positive sample even if it is not worn as long as it appears in the prediction frame, which is the opposite of our monitoring needs. On the other hand, existing object detection networks are generally complex, and the number of parameters is generally high. Such problems can lead to higher computational costs and make it challenging to meet the needs of real-time detection. This paper focuses on how to accurately and quickly detect workers' helmets. In this paper, the research objective is to identify whether all the people on a construction site are wearing helmets. At the same time, the mainstream object detection methods are computationally expensive and have low accuracy.