Research on Intelligent Wearable Detection Method Based on Machine Vision

Shao, Xintong; Li, Haixia; Liu, Shengxian

doi:10.1109/iccasit53235.2021.9633505

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…At present, many scholars carry out research on concrete crack detection, Fang Xi [1] proposed a concrete crack identification method based on edge detection algorithm. Shao Jiang [2] proposed a machine vision based automatic detection method of cracks in concrete bridges. Qingqi Li [3] proposed a self-coding based concrete crack recognition method, avoiding the crack-free image to be coded by the nearest neighbour to change the representation in the hidden space.…”

Section: Introductionmentioning

confidence: 99%

Concrete crack identification and detection method based on improved CNN and DoG operator

Luo,

Wu,

Liang

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Concrete will produce cracks under the long-term action of loads and affect the building safety, due to the unsatisfactory accuracy and efficiency of manual detection of concrete cracks, a concrete crack recognition and detection method based on improved CNN and DoG operator is proposed. Firstly, the recognition ability is trained by CNN to locate the valuable images from the image dataset, and then the locating crack images are greyscaled, denoised using bilateral filtering method, considering that the filtering will make the image edges blurred, the DoG operator is used to detect the completeness of the edges, and then the image is binary transformed by selecting thresholds through the one-dimensional Otus segmentation method, and the binary map is opened by the open operation, to fill in the broken parts within the cracks and protect the crack edges, and finally the length, width, and rotation angle of the crack are calculated by mapping the complete straight lines present in the crack through Hough space. The experimental results show that the method can accurately identify and detect crack features of different shapes with superior detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Concrete crack identification and detection method based on improved CNN and DoG operator

Luo,

Wu,

Liang

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Real-time Implementation of YOLO V5 Based Helmet with Number Plate Recognition and Logging of Rider Data using PyTorch and XAMPP

R D,

et al. 2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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YOLO V8: An improved real-time detection of safety equipment in different lighting scenarios on construction sites

Upadhyay,

Sankrityayan

2024

Preprint

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In this work we aimed to detect the safety equipment worn by the workers on construction site using the YOLOv8 model. Its a state-of-the-art deep learning model recognized for its speed and accuracy, in detecting objects within dynamic construction environments. Focusing on classes such as Helmet, Vest, Gloves, Human, and Boots, we assess YOLOv8's efficacy in real-time safety hazard detection. The classes have been labelled using the labelImg software for training the model, with that the testing of different images and videos have been carried out. After deploying the trained model, it shows an impressive accuracy rate of approximately 98.017% with the YOLOv8 model, surpassing previous iterations. Additionally, our Recall and Precision values achieve high levels at 94.9% and 94.36% respectively, while the F1 score and mean Average Precision (mAP) values approximate 91% and 91.9% respectively. These robust performance metrics underscore the reliability and effectiveness of YOLOv8 compared to other existing YOLO models, marking a significant advancement in object detection for construction site management.

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Research on Intelligent Wearable Detection Method Based on Machine Vision

Cited by 3 publications

References 3 publications

Concrete crack identification and detection method based on improved CNN and DoG operator

Concrete crack identification and detection method based on improved CNN and DoG operator

Real-time Implementation of YOLO V5 Based Helmet with Number Plate Recognition and Logging of Rider Data using PyTorch and XAMPP

YOLO V8: An improved real-time detection of safety equipment in different lighting scenarios on construction sites

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