**1. Introduction**

Working at heights is associated with frequent injuries and the deaths of workers during construction activities [1]. Fatal injuries due to construction accidents exceed 60,000 injuries every year all over the world [2]. According to the Occupational Safety and Health Administration, a similar trend occurs in developed countries, even though infrastructure construction has almost been completed in these countries [3]. Statistics from the Ministry of Housing and Urban-Rural Development of China show that, from 2010 to 2019, there were an average of 603 production safety accidents per year, resulting in approximately 730 worker deaths per year [4]. Among these accidents, fall-from-height accidents accounted for at least 52.10%, followed by struck-by-object accidents (13.90%, Figure 1). Many researchers have noted that the root causes of safety accidents are workers' unsafe behaviors [5–7]. Heinrich's accident causation theory states that more than 80% of safety accidents are caused by workers' unsafe behaviors [8]. Therefore, management to minimize workers' unsafe behaviors is important to construction safety.

Behavior-based safety (BBS) plays an influential role in the supervision and management of workers' activities [5,9,10], in which workers' activities are recorded and their behaviors are analyzed through observation, interview, and survey. Most BBS studies have involved four necessary steps [11]: (1) create a list of workers' unsafe behaviors; (2) observe and record the frequency of unsafe behaviors; (3) educate and intervene in workers' behavior; (4) provide feedback and perform follow-up observations. BBS has gained its status in construction management because it has been more successful than other methods for solving problems caused by unsafe behaviors. Furthermore, researchers

**Citation:** Hu, Q.; Bai, Y.; He, L.; Huang, J.; Wang, H.; Cheng, G. Workers' Unsafe Actions When Working at Heights: Detecting from Images. *Sustainability* **2022**, *14*, 6126. https://doi.org/10.3390/su14106126

Academic Editors: Srinath Perera, Albert P. C. Chan, Xiaohua Jin, Dilanthi Amaratunga, Makarand Hastak, Patrizia Lombardi, Sepani Senaratne and Anil Sawhney

Received: 25 March 2022 Accepted: 11 May 2022 Published: 18 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

have recognized unsafe behaviors as the most important problem. The purpose of normal science is neither to discover new types of phenomena nor to invent new theories [12]. On the contrary, normal science research is to continuously improve phenomena and theories provided by existing paradigms, which are eternal challenges to researchers' skills and imagination [13]. BBS observation is a traditional form of worker behavior measurement, which has certain limitations in practical applications [14].

**Figure 1.** Distribution map of the causes of death during construction from 2010 to 2019 [4].

Observation—the second stage—is important because it can provide more data for the analysis of patterns. Ref. [15] puts forward a safety assessment method of leading indicators based on jobsite safety inspection (JSI) through a lot of accident data analyzing. Traditional unsafe behavior observation mainly relies on safety managers' manual observation and recording, which not only consumes a lot of time and cost, but it is also difficult to cover the whole construction site, or all workers. On the one hand, many human resources are needed for data acquisition due to large sample data requirements [16]. On the other hand, excessive reliance on workers' observations can easily cause personal impact since different people have different feelings about the same thing [17]. Therefore, an automated and reliable method that could efficiently measure unsafe behavior is needed to support BBS observation. Automation technology is already making its mark in the observations of workers' behaviors [18]. Proposed real-time positioning systems based on different types of sensors and the Internet of Things (IoT) have played considerable roles in workers' safety observations [19,20]. However, sensors can sometimes affect workers' normal work [21]. Computer vision technology can also be used for collecting and processing workers' safety information [22]. Its ability to provide a wide range of visual information at a low cost has attracted a lot of attention [23–26].

Construction workers' unsafe actions are a type of unsafe behavior that could be the main reason leading to construction accidents, primarily occurring when working at heights. Most unsafe actions are instantaneous, and therefore, it is difficult for safety supervisors to observe them in real time. Furthermore, detecting workers' unsafe actions is critical to the observation process of BBS. Computer vision technology for the automatic recognition and detection of workers' unsafe actions could tentatively replace manual observations of BBS. The gap in research could be significant for specific groups of construction workers. To date, there is no automatic method of detecting the unsafe behavior of workers in a high working environment. Therefore, the present study is to help improve the observation method of workers' unsafe behavior considering five unsafe actions that mostly appeared in the high working environment on construction sites. A series of experiments involving over 30 testees were implemented to verify the proposed method. This paper is structured as follows: First of all, the research method is presented, involving unsafe actions lists, dataset construction, and the Convolutional Neural Networks (CNN) model built. Following this, the results are presented and discussed. Finally, a conclusion is drawn.
