Deep-Hart: An Inference Deep Learning Approach of Hard Hat Detection for Work Safety and Surveillance

Casuat, Cherry D.; Merencilla, Nino E.; Reyes, Ryan Carreon; Sevilla, Rovenson V.; Pascion, Cherry G.

doi:10.1109/icetas51660.2020.9484208

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…An annotation software, LabelImg [17], was used to generate the ground truth, which can handle multiple formats supported by deep learning-based algorithms [18]. The first step in the software was the definition of the label types, and, after annotation, we saved the data for each image in YOLO format (see Figure 8).…”

Section: Generating Dataset Images Using Rendering Techniquesmentioning

confidence: 99%

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

et al. 2022

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In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.

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Section: Generating Dataset Images Using Rendering Techniquesmentioning

confidence: 99%

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

et al. 2022

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“…The method achieved an F1 score of 0.75. Casuat et al [10] used Yolov3 to detect people wearing helmets. It has been reported that the model achieved an average accuracy of 79,246 in the tested dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the studies conducted in previous years suggested methods such as RCNN, faster RCNN [5,7,8] that can be highly accurate, but not so fast for real-time use. In addition, there are approaches using the SSD [4] and Yolo [6,10,12] families, which are widely used in real-time applications. In object detection, YOLO algorithms with very high accuracy and speed have been used in multiple detection tasks [13,14].…”

Section: Motivationmentioning

confidence: 99%

An Efficient and Fast Lightweight-Model with ShuffleNetv2 Based on YOLOv5 for Detection of Hardhat-Wearing

Cengil¹,

Çınar²,

Yıldırım³

2022

RCES

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Traumatic brain injuries and collisions from falls and electric shocks are among the leading causes of construction deaths. Helmets play an important role in protecting working people from accidents. However, wearing a hard hat in real life is often not strictly enforced among those who try. Therefore, it is important to check this and ensure that a helmet is worn. Today, the use of artificial intelligence-based object recognition systems has become widespread due to the advantages it provides. In this article, a one-step object detection approach based on deep learning is proposed to detect helmet use and control helmet wearing status. The model is based on the YOLOv5 architecture. In the feature extraction step of the method, ShuffleNetv2, which is a lightweight model for a fast detector, is used. The presented model has been examined on the Hard Hat Workers dataset. The architecture provided a recall value of 0.942 precision 0.91 in the corresponding dataset. The obtained results showed that the recommended model is suitable for use on construction sites to check whether a helmet is fitted.

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Research on Safety Belt Wearing Detection Algorithm based on Artificial Intelligence Working at Altitude

Xiang,

Yang,

Sun

et al. 2023

2023 2nd International Conference on Artificial Intelligence and Blockchain Technology (AIBT)

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Deep-Hart: An Inference Deep Learning Approach of Hard Hat Detection for Work Safety and Surveillance

Cited by 4 publications

References 13 publications

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

An Efficient and Fast Lightweight-Model with ShuffleNetv2 Based on YOLOv5 for Detection of Hardhat-Wearing

Research on Safety Belt Wearing Detection Algorithm based on Artificial Intelligence Working at Altitude

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