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Artificial Intelligence (AI) and Machine Learning (ML) have become increasingly popular technologies among the masses. Even not-so-tech-savvy people are exposed to these cutting-edge technologies in one way or the other.
While AI refers to a broad concept in which machines can perform tasks that are normally performed by humans, ML is a subset of AI and is based on the idea that machines should be able to learn and adapt via experience. In addition, Deep Learning is a subset of Machine Learning in which algorithms are taught using Artificial Neural Networks (ANNs).
Application of Deep Learning Models
Deep Learning can be beneficial to many industries including construction, finance, medicine, transportation, etc. They are, however, primarily focused on solving the following three fundamental issues:
- Computer Vision: It is the process of teaching machines to understand visual data, such as images or video, and to perform appropriate actions depending on what they observe. E.g. construction safety, photographic reconstruction, etc.
- Natural Language Processing (NLP): It is the programming of machines to evaluate human language via text or voice recordings. Examples include chatbots, auto-translation, legal document analysis, etc.
- Regression: AI is trained to predict a number or a score that will provide useful information to the user. E.g. regression applications, stock price prediction, fraud detection, etc.
The amount of data we generate today, which is then used to train and improve computer vision, is one of the driving factors behind the growth of computer vision.
Despite the benefits it offers, computer vision is an incredibly complex technique to bring to actualization. There are three major types of computer-vision problems: Image Classification, Object Detection, and Image Segmentation.
- Image Classification: The primary objective of image classification models is to forecast how an image will be represented in general. Even though they have limited applications in real-world problems, they were the first models that revolutionized and led to the popularity of Deep Learning. These algorithms take images as input and predict a category that represents what the image represents. For example, if we enter an image of a dog or a cat, the image classification algorithm will "classify" that image as the animal category that appears on it.
- Object Detection: For every known object within an image, these algorithms determine the object category and detect the positions of these objects using bounding boxes. Object detection algorithms take an image as input and return a projected category, while image classification algorithms take images as input and produce images. However, the predicted bounding box positions will appear in the generated photos.
- Image Segmentation: In general, segmentation challenges in computer vision are more difficult than other problems as the algorithms used in segmentation work at the pixel level. Rather than predicting what a group of pixels represents, the algorithms try to predict the category of each individual pixel in the given space.
Images are used as input and output in image segmentation models; however, the output images will have a predicted "layer" superimposed on top of them to represent the category of each pixel.
Instance segmentation is similar to semantic segmentation, with the exception that it goes one step further in solving the general segmentation problem in computer vision. As a result, it's a little more advanced and adds a layer of complexity.
It is pretty evident that object detection technology can be of tremendous help to the construction industry. Detecting objects in a complex setting is the most fundamental step in understanding and interpreting the construction scene's context (i.e., layout, structure) and establishing functional, functional, and semantic links between those objects.
The technology can be applied in autonomous construction where unmanned vehicles need to identify and avoid objects to navigate through the site and perform tasks. Similarly, robots must recognize certain objects for performing operations.
The Long Road Ahead
In order to truly utilize AI in construction, the ability to detect objects in real-time (or near real-time) is of utmost importance in some applications. For instance, preventing possible mishaps requires real-time identification of risky behavior such as a human crew working in close proximity to a site hazard or moving object.
But to track the motion of objects in a live video feed, you'll need a very fast algorithm that can analyze each video frame in rapid succession and discover all objects of interest in the current frame before the next one appears.
Fast and light AI algorithms can be trained on relevant and valuable data for getting the best results in this space. The quality of data in supervised machine learning (ML) is determined by how effectively it has been annotated for model training.
As the AI model must be trained with a diverse dataset in order to detect objects of various appearances in real-world scenarios, the training and testing images should be gathered from a variety of sources to ensure that the dataset covers a wide range of construction settings.
Deep Learning is a subset of Machine Learning in which algorithms are taught using Artificial Neural Networks (ANNs).
Natural Language Processing (NLP) is the programming of machines to evaluate human language via text or voice recordings. Examples include chatbots, auto-translation, legal document analysis, etc.
Object detection algorithms take an image of input and return a projected category, while image classification algorithms take images as input and produce images.