AI is here to stay – whether you adopt it or not. The latest advancements in machine learning and AI are more than just a flash in the pan moment, these new tools are helping us work smarter, faster and with far greater efficiency than ever before.
64% of companies that have moved to early adoption of AI have reported a significant increase in productivity. At the same time, 75% of market leaders believe that they’ll go out of business if they don’t start adopting – and this is only the beginning. With such rapid advancements in machine learning, staying on top of the latest tools and workflows can help you make the most of this growing technology, particularly within the world of reality capture.
Reality capture already leverages cutting-edge technology in the form of drones, robots and automation software to help capture, manage and glean valuable insights on indoor and outdoor sites through images consolidated into detailed orthomosaic maps and 360 walkthroughs. That said, without some kind of external context, the images captured only tell so much. Businesses often have to rely on the interpretation of humans in the loop (HITL) to define what is in the images we capture – which isn’t always perfect.
By integrating machine learning AI models, we’re able to quickly interpret the details in an image: identifying, organizing and reporting on elements of interest automatically.
The basics of AI and machine learning in image processing
What do we mean when we say AI and machine learning? To put it simply, AI is a machine’s ability to perform tasks that would typically be done by humans. AI is a broad term that covers a wide range of technologies and applications. It is built from machine learning and/or deep learning algorithms.
Deep learning involves highly complex algorithms that require very little human interaction or input to create an AI model. Machine learning, on the other hand, will involve a HITL to not just input the data to the AI model but help it interpret the data, fine-tuning the model to perform specific functions.
When it comes to image processing in DroneDeploy, machine learning is the technology that decodes images and finds patterns and actions within those images that are naked to the human eye. Our software uses algorithms to learn from and make decisions or predictions based on data – in this case, image data. In the realm of image processing, machine learning algorithms are designed to recognize patterns and features within images.
Consider the image of a cat. To a human observer, it's easy to see and identify the cat. However, to a computer without machine learning, the cat doesn't exist – it’s just pixels. The computer merely perceives a collection of orange and white pixels with no further understanding.
But with machine learning, the game changes entirely. The algorithm can be trained to recognize the specific patterns and shapes corresponding to a cat, enabling the computer to identify what’s in the image. It can determine the object's unique characteristics: the sharpness of the eyes, the curve of the tail, the texture of the fur, and so on. It is no longer a set of unassociated pixels, but an intelligent comprehension of a cat in the image.
This transformation from mere pixels to discernible objects is the crux of machine learning in image processing. It forms the bedrock of many modern applications; including autonomous vehicles, facial recognition, medical imaging and elements within our latest product release. It turns images from simple visual representations into rich sources of actionable data and insight.
By applying complex machine learning models, the software can identify and categorize elements within an image far beyond the capabilities of simple pixel-based analysis. Let's delve deeper into the steps that make it happen.
Classification
The first step in image processing using machine learning is classification. At its most basic, classification involves the task of sorting images into various categories or 'classes'. A machine learning model is trained using a set of labeled images (the training set), teaching it to recognize specific features that define each class.
Localization
Once an image has been classified, the next step is localization. Localization takes classification a step further by not only identifying what an object is, but also determining where it is in the image. This process involves defining a bounding box around the object of interest, providing spatial context within the image.
Object detection
The third step, object detection, is an extension of localization. Instead of identifying just one object in the image, object detection can identify multiple objects and their locations in the same image. It involves classifying and localizing multiple objects, providing more comprehensive image understanding.
Segmentation
The final and most complex step is segmentation. While object detection can identify objects and their general locations within an image, segmentation goes further to precisely delineate the boundaries of each object.
Solar AI construction progress reports in DroneDeploy
Turning pixels into insights
For DroneDeploy, imagery is our bread and butter. However, without intelligence and data context, the images that you capture both in the air and on the ground are just a bunch of pixels. This is where machine learning comes in.
By introducing machine learning to your imagery, we help you derive more valuable information from each image. Immediately, the orthomosaic, 360 photo or pano you captured has a far richer context, providing insight into the critical nuances of your project. From utility solar farms to construction job sites, AI combined with imagery and data helps you catch mistakes early, eliminate rework and develop faster and smarter workflows.
For example, pre-pour concrete inspection reports in DroneDeploy utilize AI product vision and machine learning to analyze your drone orthomosaic maps and identify elements that don’t match up with the original plans. This feature allows you to validate in-slab placement issues and proactively address them in the field before concrete is poured, eliminating risk and reducing costly rework.
Amplify your workflow with AI
The value machine learning and AI computer vision bring to reality capture is immeasurable. They transform the process from simple image collection to actionable insight generation. With machine learning, every image captured offers a plethora of insights, helping industries automate and enhance their operations.
To learn more about how to utilize machine learning and AI in your reality capture workflows, check out the live recording of our recent product release livestream.
About the Author
Content Marketing Manager - DroneDeploy
Conner Jones currently manages content marketing at DroneDeploy. Conner helps tell the DroneDeploy story across a wide array of social and web channels, showcasing the power of drones and reality capture in a diverse range of industries.