# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 1 Introduction  
Given the continued rapid growth of modern society worldwide, the construction industry has developed increasingly fast. However, as well as the traditional hot issues such as design and construction technology, the safety of construction sites is becoming one of the hottest topics in the current construction industry. The construction industry is one of the most prone to safety accidents among all industries. Over the past 20 years, the construction industry has experienced a decline in accident rates, but the industry's accident rate is still about three times that of all industries [1]. Therefore, it is of great practical significance to study the safety guarantee in the construction industry. Among the safety accidents in the construction industry, the level of disability caused by head injury is the highest, thus, reducing head injuries is obviously the primary objective to ensure the safety of personnel [2]. Currently, the helmet is the most effective way to reduce head injuries because the impact resistance of the helmet can disperse the impact of weights when an accident occurs, so as to greatly reduce the head and neck injuries caused by impact. In order to protect the life safety of employees, the common rule is to force people to wear helmets before entering the construction area and starting working. Practically, it is difficult to implement this requirement since the workload of maintaining supervision is tedious and labor-consuming. Consequently, several kinds of monitoring systems have been developed including fixed camera-based systems and moving camera-based processes. These systems work in a closed loop, which captures information through cameras and performs analysis manually or automatically to produce alarms or any other control signals. It is obvious that moving cameras are more flexible and able to cover wider regions so these have attracted a larger amount of attention from users and researchers. Nonetheless, moving cameras, either cameras on-board an unmanned aerial vehicle (UAV) or web cameras, normally suffer from the problem of data transmission, according tothe fact that monitoring image or video files always have a large size and ask for a wide bandwidth, which is a precious resource in wireless communication. To overcome this problem, compressing files by decreasing the resolution can be performed, but this often degrades the image quality, which is hard to be avoided during the trans  
Mission, as shown in Figure 1. This ongoing chain reaction means that the difficulties of monitoring and analyzing images or videos are significantly increasing. In all honesty, this is not a big issue for manual analysis since human brains can adapt to this degrading in most cases, but it really affects the performance of most automatic analyzing methods. Taking into consideration the fact that automatic methods surpass manual approaches due to their advantages of being more effective and less labor-consuming, there is an urgent need for the improvement of automatic helmet detection performance based on low-resolution image or video files, which is the motivation of this paper. In recent years, there have been a large number of achievements focused on the automatic detection of helmets. Based on the employed research ideas, these methods can be divided into two kinds. Some of them use traditional solutions of object detection tasks, which consist of handcraft feature designing and machine learning models such as a support vector machine. The predesigned features are usually from general computer vision tasks, such as Haar-like features from face detection [3], and the deformable parts model (DPM), which could cascade different single features [4]. However, choosing or designing features is complicated, easy to be prone to poor accuracy and difficult to be extended from one scenario to another. The other methods use deep learning to solve object detection.