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Results indicate that performance is higher if the model is trained on both crowdsourced and web-mined images.
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To assess the agility of object detection, transfer learning is used to train two variations of this model, namely, YOLO-v2 and YOLO-v3, and test them on different data combinations (crowdsourced, web-mined, or both). First, a large-scale image dataset, named Pictor-v2, is created, which contains about 3,500 images and approximately 11,500 instances of common construction site objects (e.g., building, equipment, worker).
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This paper investigates YOLO-based CNN models in fast detection of construction objects. One of the most promising DL algorithms that balance speed and accuracy is YOLO (you-only-look-once). While lightweight DL algorithms (e.g., Mask R-CNN) can perform visual recognition with relatively high accuracy, they suffer from low processing efficacy, which hinders their use in real-time decision-making. The accuracy requirement, however, may offset the computational speed of the candidate method. A fundamental step toward machine-driven interpretation of construction site scenery is to accurately identify objects of interest for a particular problem. This has motivated new research on soft computing methods that utilize high-power data processing, computer vision, and deep learning (DL) in the form of convolutional neural networks (CNNs).
Capturing reality orthographic image target file manual#
Manual interpretation of such colossal amounts of data, however, is non-trivial, error-prone, and resource-intensive. A large volume of images and videos is collected from construction projects every day to track work progress, measure productivity, litigate claims, and monitor safety compliance. Among different technologies, vision-based sensors are by far the most common and ubiquitous. Sensing and reality capture devices are widely used in construction sites.