Artificial Intelligence and Machine Learning have seen a massive growth in the past few years, and have reached a market size of over 22 billion US dollars. While there are numerous domains AI interacts very closely with, Computer Vision is and has been one of the most significant ones, acting as a catalyst for this immense growth.
At its core, computer vision deals with teaching and enabling systems to extract meaningful information, patterns, and insights from digital visual inputs such as images and videos. Using these inputs, complex algorithms and techniques are employed to allow machines to “see” by deriving information from a mathematical representation of the world. Often, these systems then take important actions based on these inputs and insights derived from them.
As humans, we spend many years of our lives understanding the visual stimuli that the world around us provides us, and picking out the information that is important to us. Along the same lines, machines need this visual stimuli, and an extensive amount of it, in order to determine patterns and pick out the most appropriate information. Without the biological apparatus that humans have, their best source of visual stimuli is data, more specifically: annotated data.
Often, computer vision systems need a large amount of image-like data that has been carefully labeled and processed. These annotated images, can have a variety of elements, such as:
- Classifications: Categorizing an image as a whole, to belong to a certain category.
- Bounding boxes and Polygons: Localizing certain objects of classes within an image
- Lines and Points: Specifying pixels belonging to a certain class within an image
A detailed discussion upon the topic of image annotation can be found in one of our prior blog posts.
These elements allow the system to separate the pixels, classes and regions of importance within an image, effectively picking out the “signal from the noise”. Learning from these patterns the system overtime adapts to become better at recognizing these patterns from unknown instances and providing deeper and more accurate insights.
Major Use Cases
While image annotation can be employed for countless applications, there are certain areas which can derive a lot of value from such annotated data and computer vision systems. As a result, most of the work in present times is being devoted to these fields.
While each of the areas mentioned below can have an extensive article upon their sub-application areas, we will give a high level overview of the healthcare, transportation and agriculture industries, and their use cases for image annotation.
The healthcare industry relies heavily on visual detection of various conditions, diseases and ailments. With the help of computer vision / machine learning systems and quality annotated data they can derive a lot of value. Specifically, the following areas within the healthcare industry have seen rapid and effective input:
- COVID-19 diagnosis: After the start of the pandemic, a plethora of applications for automated COVID-19 detection were created. A number of quality datasets were produced covering a wide variety of medical scans such as CT-Scans and MRIs, many classifying the scans based on COVID-19 PCR test being positive or negative. This effectively allowed many systems to discover correlations between patients’ scans and their COVID-19 status. COVID-Net is an example of this.
- Face Mask Detection: In various scenarios, wearing masks is crucial to prevent any health risks posed to others. During the current pandemic, governments and organizations have posed fines and penalties for the neglect of wearing masks. To ensure this systems are developed that detect whether an individual is wearing their face-mask or not. At Ango AI we published a dataset open to the public containing such images, labeled with the highest quality and tested an internal QA system which performed effectively at determining which individuals were with or without masks.
- Tumor Detection: This is another area closely related to the field of radiology, hence an ideal candidate for machine learning systems relying on image inputs. Annotated images depicting the regions where tumor or any such malicious body is present can help systems learn the patterns that exist and help doctors in diagnoses. Since tumors are often identifiable within a scan this is specially convenient for annotation and consequently detection systems.
Transportation is a massive sector that has often been one of the first ones to accommodate technological progress and innovation. Many sub-sectors have already seen incredible research, ranging from autonomous driving to parking occupancy detection. Here are a few such areas that have seen concrete results by using AI systems relying on annotated images and videos:
- Autonomous Driving: Massive datasets have been created with high quality annotated images and videos to allow for self-driving vehicles to be able to identify various key elements crucial to safe and effective navigation on the road. These range from identifying and localizing objects such as other vehicles, traffic signs and signals, driving lanes e.t.c. These elements help the vehicle learn the patterns required to take the ideal course of action in unknown conditions.
- Traffic Flow Analysis: Traffic flow analysis often requires the use of invasive technologies such as sensors under the road. However, with systems now being able to identify cars with extremely high accuracy, surveillance cameras can give accurate insights into the traffic flow, giving indicators of congestion, road blocks, accidents and much more. Systems , relying on data that allows detection of various traffic elements, can accurately count the number of vehicles passing within a timeframe and can allow traffic engineers to take measures accordingly.
- Parking Occupancy Detection: Given annotated data depicting localized vehicles or empty vs full parking slots, machine learning systems can accurately predict which parking slots are empty and can be used as part of Parking Guidance and Information (PGI) systems. Datasets such as PKLot contain thousands of images related to parking data which can be ingested by deep learning algorithms to allow for such functionality.
Agriculture is another key area that has benefited immensely from the progress in computer vision systems and the availability of large scale agricultural datasets. Camera surveillance and massive fields produces a large amount of data, which can be annotated and processed by AI systems to provide critical insights such as disease and pest detection, crop and yield monitoring, and livestock health monitoring:
- Disease and Pest Detection: Traditional Detection methods for both disease and pest involve manually observing each plant. However, with availability of annotated data that localizes and classifies plants with diseases and pests, numerous models can now be trained to predict not only the presence of the disease and pest but also their location and severity, allowing farmers to take prompt action.
- Crop and Yield monitoring: With large scale farms it becomes increasingly difficult to monitor the complete area, and while surveillance systems make the task easier there still needs to remain a layer of human super supervision to oversee the state of the crops. Various datasets exist that depict various states and statistics of crop growth, such as classification based on ripeness. These cut down the surveillance time significantly and allow the farmers to take action when required by the crops.
- Livestock Health Monitoring: Similar to crop surveillance and disease detection, monitoring of livestock health is a pressing issue, also traditionally requiring direct human observation. With data that characterizes not only the types of various livestock animals but also, sick and healthy animals and various types of diseases within them systems can be built to use the insights within this data to tackle novel situations where livestock may be sick and in need of medical care. The task has allowed systems to assist livestock farmers in keeping track of animal’s health.
With countless opportunities that arise with the interaction of Artificial Intelligence and Computer vision it is evident that quality annotated data is key in any such application. As machines learn to “see” the world they need the right direction and mentorship and that comes through annotated data, once one has access to this data the possibilities are endless.
The domains discussed above are simply a subset of a massive industry that relies heavily on annotated images. Machine Learning systems and the use cases for image annotation include manufacturing, mining, irrigation, construction, defect detection, workforce monitoring, product assembly, predictive maintenance, document classification and recognition, and much more.
Image annotation is one of Ango AI’s core offerings. Whether you need a platform to annotate images with your team, on the cloud or on-premises, or if you’re looking for a high-quality yet simple, fully-managed end-to-end data labeling solution, Ango AI provides it. Try our platform at hub.ango.ai, or contact us to learn more about how we can help you solve your data labeling needs.
Author: Balaj Saleem
Technical reviewer: Onur Aydın
Editor: Lorenzo Gravina
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