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• W4385416706 startingPage "6826" @default.
• W4385416706 abstract "One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regard to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real-time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue saturation value. Additional filtering methods regarding the non-welding zone and a contour area-based filter were also newly developed to enhance the accuracy of welding spark prediction. The method was evaluated using 230 welding spark images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding spark detection to enhance fire surveillance at construction sites." @default.
• W4385416706 created "2023-08-01" @default.
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• W4385416706 date "2023-07-31" @default.
• W4385416706 modified "2023-10-18" @default.
• W4385416706 title "Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters" @default.
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• W4385416706 doi "https://doi.org/10.3390/s23156826" @default.
• W4385416706 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37571610" @default.
• W4385416706 hasPublicationYear "2023" @default.
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