Deep-learning-based visual data analytics for smart construction management

The large extension and complex structure of most industrial and construction areas very often make it unfeasible or inconvenient for human operators to constantly survey all the workers to detect those who do not properly wear their Personal Protection Equipment (PPE) devices. However, such a detection is of utmost importance to reduce the number of worker injuries. Consequently, the adoption of a computer vision system based on Deep Neural Networks (DNNs) that performs PPE detection by analysing the video streams from surveillance cameras is an appealing option. For instance, smart video cameras placed in the workplace might process the video frames at run-time and trigger alarms whenever they detect workers not correctly wearing PPE devices. However, in order to be sufficiently accurate, DNN-based object detection requires a high computational power that is difficult to embed in cameras. Moreover, DNN training has to be done on a large dataset with thousands of labeled image samples, and therefore the creation of a customized DNN to detect special PPE devices requires a huge effort in finding and labeling images to train the network. This paper proposes a PPE detection framework that combines DNN-based object detection with human judgement through fuzzy logic filtering. The proposed framework runs in near real-time on embedded devices and can be trained with a low number of images (i.e., few hundreds), still providing good accuracy results.

Personal Protection Equipment detection system for embedded devices based on DNN and Fuzzy Logic

Deep learning methods for object detection in smart manufacturing: A survey

The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students.

https://www.mdpi.com/1424-8220/22/22/8820/pdf?version=1668500427

Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions

Generic compliance of industrial PPE by using deep learning techniques

Construction safety is a matter of great concern for both practitioners and researchers world-wide. Even after enough risk assessments and implementations of adequate controls on the work environments, construction workers are still subject to safety hazards. The need for personal protective equipment (PPE) becomes important in this context. Automatic and real-time detection of non-compliance of workers towards PPE is an important concern. The developments in the field of computer vision and data analytics, especially using deep learning algorithms have the potential to address this challenge in construction. Through this study a framework is developed to sense in real time, the safety compliance of construction workers with respect to PPE, thereby allowing to integrate this framework into the safety workflow of an organization. The study makes use Convolutional Neural Networks model developed by applying transfer learning to a base version of YOLOv3 deep learning network. Based on the presence of hardhat and safety jacket, the model predicts the compliance in four categories such as NOT SAFE, SAFE, NoHardHat and NoJacket. A data set of 2509 images collected from video recordings from several construction sites and web-based collection has been used to train the model. The model reported an F1 score of 0.96 with an average precision and recall rates at 96% on test data set. Once a non “SAFE” category is detected by the model, an alarm and a time-stamped report are also incorporated to enable a real-time integration and adoption on the construction sites. Overall, the study provides evidence on the feasibility and utility of computer vision-based techniques in automating the safety related compliance processes at construction sites.

/pdf/detection-of-personal-protective-equipment-ppe-compliance-on-2an2xe7app.pdf

R. Sankarlal

Papers

Detection of Personal Protective Equipment (PPE) Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques