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- W3145738497 abstract "Visual recognition of the content and actions that take place in a construction site is important in many applications such as data-driven simulation, autonomous systems, and intelligent machinery. Construction project, however, are dynamic and complex, and often take place in harsh environments. This may hinder the ability to collect good quality, well-lit, and occlusion-free imagery, which in turn, can lower the performance of computer vision models for fast and reliable object detection. In this paper, we propose and validate a deep convolutional neural network (CNN)-based generative adversarial network (GAN) trained and tested on construction site photos from two in-house datasets to increase image resolution by generating missing pixel information. Results show that using GAN-enhanced images can improve the average precision of pre-trained models for detecting objects such as building, equipment, worker, hard hat, and safety vest by up to 32% while maintaining the overall processing time for real-time object detection." @default.
- W3145738497 created "2021-04-13" @default.
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- W3145738497 date "2020-12-14" @default.
- W3145738497 modified "2023-10-14" @default.
- W3145738497 title "Deep Generative Adversarial Network to Enhance Image Quality for Fast Object Detection in Construction Sites" @default.
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- W3145738497 doi "https://doi.org/10.1109/wsc48552.2020.9383890" @default.
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