Automatic Detection of Helmet Uses for Construction Safety

Rubaiyat, Abu Hasnat Mohammad; Toma, Tanjin Taher; Kalantari-Khandani, Masoumeh; Rahman, Syed Ashiqur; Chen, Lingwei; Ye, Yanfang; Pan, Christopher S.

doi:10.1109/wiw.2016.045

Cited by 119 publications

(54 citation statements)

References 18 publications

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“…With the development of computer technology, automatic visual detection has been widely used in industrial applications. Many related studies have been conducted for helmet wearing detection [4], [5]. Kalantari et al [4] divided the entire helmet wearing detection process into two parts.…”

Section: Introductionmentioning

confidence: 99%

“…The method achieved a certain detection effect; however, the overall accuracy of the method is low and only a specific color safety helmet can be detected. Wu et al [5] utilized the Local Binary Patterns(LBP), Hu Moment Invariant(HMI) and Color Histogram(C.H.) of the image to extract the feature of different-color helmets, then hierarchical Support Vector Machine (SVM) is used to recognize safety helmets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Incorporate Online Hard Example Mining and Multi-Part Combination Into Automatic Safety Helmet Wearing Detection

Lyu

et al. 2021

IEEE Access

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporate Online Hard Example Mining and Multi-Part Combination Into Automatic Safety Helmet Wearing Detection

Lyu

et al. 2021

IEEE Access

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“…Safety helmet belongs to one of the crucial PPEs of construction engineering, and it is an effective PPE to protect workers from being struck by falling objects (Rubaiyat et al., 2016; Suderman, Hoover, Ching, & Scher, 2014). Moreover, wearing a safety helmet on a construction site is a statutory requirement all over the world (Cabahug, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the most commonly deployed sensor for automatic detection on a construction site is the imaging sensor using visible light. Many algorithms have been proposed to solve the problem of safety helmet detection and similar applications in videos and images (W. Fang, Ding, Luo, & Love, 2018; Park, Elsafty, & Zhu, 2015; Rubaiyat et al., 2016; Seong, Son, & Kim, 2018; Shrestha, Sherestha, Bajracharya, & Yfantis, 2015). However, the imaging sensor‐based method is hugely challenged by some factors such as interocclusion, scale variation, perspective distortion, and small object detection.…”

Section: Introductionmentioning

confidence: 99%

Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning

Shen

Xiong

et al. 2020

Computer aided Civil Eng

101

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Detecting safety helmet wearing in surveillance videos is an essential task for safety management, compliance with regulations, and reducing the death rate from construction industry accidents. However, it is much challenged by some factors like interocclusion, scale variances, perspective distortion, small object detection, and the carrier recognition of safety helmet. Traditional image‐based methods suffer from them. This article proposes a new methodology for detecting safety helmet wearing, which makes use of convolutional neural network‐based face detection and bounding‐box regression for safety helmet detection. On the one hand, the method can help to recognize the carrier of the safety helmet and detect a multiscale and small safety helmet. On the other hand, deep transfer learning based on DenseNet is introduced and applied using two different strategies, namely, object feature extractor and fine‐tuning for safety helmet recognition. To further improve the recognition accuracy, the network model with two peer DenseNet networks is trained by mutual distillation. Extensive analysis and experiments show that the novel methodology has considerable advantages in detecting safety helmet wearing compared to other state‐of‐the‐art models. The proposed model has achieved 96.2% recall, 96.2% precision, and 94.47% average detection accuracy. These results, precision‐recall (PR) curve, and receiver operating characteristic (ROC) curve demonstrate the feasibility of the new model.

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“…Previous computer vision algorithms for helmet detection are usually used in relatively simple scenes. For helmet detection, Rubaiyat et al, (2016) used a histogram of oriented gradient and a support vector machine to locate persons, then used a hough transform to detect helmet for the construction worker. Li et al (2017b) identified helmets by background subtraction.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based ensemble method for helmet-wearing detection

Fan

Peng

Dai

et al. 2020

PeerJ Computer Science

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Recently, object detection methods have developed rapidly and have been widely used in many areas. In many scenarios, helmet wearing detection is very useful, because people are required to wear helmets to protect their safety when they work in construction sites or cycle in the streets. However, for the problem of helmet wearing detection in complex scenes such as construction sites and workshops, the detection accuracy of current approaches still needs to be improved. In this work, we analyze the mechanism and performance of several detection algorithms and identify two feasible base algorithms that have complementary advantages. We use one base algorithm to detect relatively large heads and helmets. Also, we use the other base algorithm to detect relatively small heads, and we add another convolutional neural network to detect whether there is a helmet above each head. Then, we integrate these two base algorithms with an ensemble method. In this method, we first propose an approach to merge information of heads and helmets from the base algorithms, and then propose a linear function to estimate the confidence score of the identified heads and helmets. Experiments on a benchmark data set show that, our approach increases the precision and recall for base algorithms, and the mean Average Precision of our approach is 0.93, which is better than many other approaches. With GPU acceleration, our approach can achieve real-time processing on contemporary computers, which is useful in practice.

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Automatic Detection of Helmet Uses for Construction Safety

Cited by 119 publications

References 18 publications

Incorporate Online Hard Example Mining and Multi-Part Combination Into Automatic Safety Helmet Wearing Detection

Incorporate Online Hard Example Mining and Multi-Part Combination Into Automatic Safety Helmet Wearing Detection

Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning

A deep learning-based ensemble method for helmet-wearing detection

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