Convolutional neural networks: Computer vision-based workforce activity assessment in construction

TL;DR: In this paper, the authors present a review of state-of-the-art methods in a typical vision-based scheme, and discuss challenges associated with their application, aiming to guide practitioners to find suitable approaches for a particular project.

TL;DR: It is suggested that proposed computer-vision approach that is developed can be used by site management to automatically identify unsafe behavior and provide feedback to individuals about their likelihood of falls from heights by recognizing unsafe behavior in real-time.

TL;DR: This paper designs and develops a deep learning and computer vision-based framework for safety in construction by integrating an array of digital technologies with multiple aspects of data fusion and identifies the research challenges that can materialize with using deep learning to identify unsafe behavior and work conditions.

TL;DR: Performance monitoring of equipment and workers in the construction industry would help project managers improve the productivity rates of construction projects.

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.

TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.