PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites

Ferdous, Md. Shafiul; Ahsan, Sk. Md. Masudul

doi:10.7717/peerj-cs.999

Cited by 29 publications

(8 citation statements)

References 27 publications

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“…For instance, the Pictor-v3 [34] dataset emphasizes ensuring the proper wearing of safety helmets and vests by individuals. Lastly, there are combinations of the previous three categories, such as the Color Helmet and Vest (CHV) [35], CHVG [36], and FZU-PPE [37] datasets. Although these studies have achieved favorable experimental results, there are still some limitations.…”

Section: B Object Detection Methods For Ppementioning

confidence: 99%

MARA-YOLO: An Efficient Method for Multiclass Personal Protective Equipment Detection

Di,

Xiang,

Daoqing

et al. 2024

IEEE Access

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Personal protective equipment (PPE) plays a crucial role in protecting workers from unpredictable physical threats and reducing fatal occupational injury rates. However, PPE is not always worn by workers in all cases, which poses a challenge to site management. In this context, object detection methods based on deep learning have been used to strengthen site management. To address the challenges posed by limited PPE detection performance under complex environmental conditions, we develop the MARA-YOLO object detection model, which is designed to balance speed and accuracy. Firstly, based on YOLOv8-s, we introduce a modified re-parameterizable backbone, which consists of MobileOne-S0 and an efficient downsampling block known as the attentional Space-to-Depth (AS-Block). Subsequently, we propose the R-C2F module, which fused feature maps from diverse receptive fields and enhances the model's sensitivity to the texture information of objects, as well as its ability to capture information from varying depths. Build upon R-C2F and adaptively spatial feature fusion (ASFF), a multi-scale feature fusion module RASFF is further introduced to mitigate inconsistent multi-scale outputs in the model. Finally, a dedicated dataset consisting of 2750 images covering 9 categories is constructed. The ablation experiments demonstrate that compared to the baseline, MARA-YOLO achieves a 6.7% improvement in AP 50 and a 10.2% improvement in AP 75 on the proposed KSE-PPE dataset. In the comparative experiments, MARA-YOLO achieves a mean average precision (mAP) of 74.7% on the KSE-PPE dataset, surpassing other lightweight state-of-the-art models by more than 4.95%.

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Section: B Object Detection Methods For Ppementioning

confidence: 99%

MARA-YOLO: An Efficient Method for Multiclass Personal Protective Equipment Detection

Di,

Xiang,

Daoqing

et al. 2024

IEEE Access

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“…With the continuous progress of information technology and industrial technology, modern industrial production is developing in the direction of high speed, precision and intelligence, and data-driven [16,17] abnormal event diagnosis methods are gaining more and more attention and development, including the application of visual data for PPE detection. The vision-based methods can be divided into two categories: one is the traditional method of image processing combined with machine learning [5][6][7][18][19][20]; the other is using deep learning technology, e.g., object detection [9][10][11][12][21][22][23][24][25][26][27][28]. In the traditional approach, the region of interest (e.g., the head region or torso region) is usually first located using image processing techniques, and then image features [29][30][31] are extracted and machine learning methods are used to train a classifier to determine whether the region is a helmet or workwear.…”

Section: Literature Reviewmentioning

confidence: 99%

A high-performance framework for personal protective equipment detection on the offshore drilling platform

Gong

Yuan

et al. 2023

Complex Intell. Syst.

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In order for the offshore drilling platform to operate properly, workers need to perform regular maintenance on the platform equipment, but the complex working environment exposes workers to hazards. During inspection and maintenance, the use of personal protective equipment (PPE) such as helmets and workwear can effectively reduce the probability of worker injuries. Existing PPE detection methods are mostly for construction sites and only detect whether helmets are worn or not. This paper proposes a high-precision and high-speed PPE detection method for the offshore drilling platform based on object detection and classification. As a first step, we develop a modified YOLOv4 (named RFA-YOLO)-based object detection model for improving localization and recognition for people, helmets, and workwear. On the basis of the class and coordinates of the object detection output, this paper proposes a method for constructing position features based on the object bounding box to obtain feature vectors characterizing the relative offsets between objects. Then, the classifier is obtained by training a dataset consisting of position features through a random forest algorithm, with parameter optimization. As a final step, the PPE detection is achieved by analyzing the information output from the classifier through an inference mechanism. To evaluate the proposed method, we construct the offshore drilling platform dataset (ODPD) and conduct comparative experiments with other methods. The experimental results show that the method in this paper achieves 13 FPS as well as 93.1% accuracy. Compared to other state-of-the-art models, the proposed PPE detection method performs better on ODPD. The method in this paper can rapidly and accurately identify workers who are not wearing helmets or workwear on the offshore drilling platform, and an intelligent video surveillance system based on this model has been implemented.

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“…For the application in construction site, Hayat et al [ 34 ] used YOLO v5 to detect safety helmets on construction sites and showed excellent detection performance even in low light conditions. Ferdous et al [ 35 ] detected personal protective equipment on construction sites based on YOLO family’s anchor-free architecture, YOLOX, and found. YOLOX yields the highest mAP of 89.84% among the other three versions of the YOLOX.…”

Section: Related Workmentioning

confidence: 99%

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

Xue

et al. 2023

Sensors

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The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers’ behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers’ behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers’ behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers’ behavior monitoring and management.

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PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites

Cited by 29 publications

References 27 publications

MARA-YOLO: An Efficient Method for Multiclass Personal Protective Equipment Detection

MARA-YOLO: An Efficient Method for Multiclass Personal Protective Equipment Detection

A high-performance framework for personal protective equipment detection on the offshore drilling platform

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

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