Artificial Intelligence and Machine Learning have seen 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, and image annotation is at the core of computer vision tasks.
At its core, computer vision is a field that deals with teaching and enabling systems to extract meaningful information, patterns, and insights from digital visual inputs such as images and videos. Complex algorithms and techniques allow machines to “see” using these inputs. Often, these systems then take 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 stimulus, and an extensive amount of it, to determine patterns and pick out the most appropriate information. Without the biological apparatus we have, their best source of visual stimuli is 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 category
- Bounding boxes and Polygons: Localizing specific objects of classes within an image
- Lines and Points: Specifying pixels belonging to a class within an image
A detailed discussion on image annotation is also 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. The system then accurately recognizes these patterns from unknown instances while providing insights.
Important Use Cases
While image annotation has countless applications, certain areas can derive more 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 area mentioned below can have an extensive article on their sub-application areas, we will give a high-level overview of the healthcare, autonomous vehicles, and agriculture industries and their use cases for image annotation.
The healthcare industry relies heavily on the visual detection of various conditions, diseases, and ailments. With the help of computer vision/machine learning systems and quality annotated data, healthcare companies can derive high value. Specifically, the following areas within the healthcare industry have seen rapid and effective input:
- COVID-19 diagnosis: During the pandemic, automated COVID-19 detection was the need of the hour. Several quality datasets covering a wide variety of medical scans, such as CT scans and MRIs, and many classifying the scans based on COVID-19 PCR test being positive or negative, needed annotation. It effectively allowed many systems to discover correlations between patients’ scans and their COVID-19 status.
- 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 posed fines and penalties for not wearing masks. AI/ML-based solutions were needed to detect whether an individual was wearing a mask.
- Tumor Detection: This is another area closely related to radiology, hence an ideal candidate for machine learning systems relying on image inputs. Annotated images depicting the regions where the tumor or any such malicious body is present can help systems learn the patterns and help doctors with diagnoses. Since tumors are often identifiable within a scan, this is convenient for annotation and detection systems.
The autonomous vehicles industry is a massive sector that has often been the first 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 self-driving vehicles to identify various elements crucial to safe and effective navigation on the road. These include identifying and localizing objects such as other vehicles, traffic signs, signals, driving lanes, etc. These elements help the AV learn the patterns required to take the ideal course of action in unknown conditions.
- TrafficFlow Analysis: Traffic flow analysis often requires using invasive technologies such as sensors under the road. However, with systems now able to identify cars with extremely high accuracy, surveillance cameras can give accurate insights into the traffic flow, giving indicators of congestion, roadblocks, 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, allowing 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 empty parking slots to help Parking Guidance and Information (PGI) systems
Agriculture is another 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 produce a large amount of data to be processed by AI systems for critical insights such as disease and pest detection, crop and yield monitoring, and livestock health monitoring.
- Disease and Pest Detection: Traditional Detection methods for diseases and pests involve manually observing each plant. However, with annotated data that localizes and classifies infected plants, models can be trained to predict the presence of diseases and pests with location and severity.
- Crop and Yield monitoring: With large-scale farms, it becomes increasingly difficult to monitor the complete area. While surveillance systems make the task easy, there still needs to remain a layer of human supervision to oversee the state of the crops. Various datasets exist that depict the state and statistics of crop growth, such as classification based on ripeness. These cut down the surveillance time and allow the farmers to take action.
- Livestock Health Monitoring: Similar to crop surveillance and disease detection, monitoring livestock health is a pressing issue and requires direct human observation. With data that characterizes the types of various livestock animals and 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 tracking animal health.
With countless opportunities arising with the interaction of Artificial Intelligence and Computer vision, it is evident that quality annotated data is crucial in any such application. As machines learn to “see” the world, they need the right direction and mentorship from annotated data.
The domains discussed above are 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 iMerit’s core offerings. Whether you need a platform to annotate images with your team, on the cloud, or on-premise, or if you’re looking for a high-quality yet simple, fully managed end-to-end data labeling solution, iMerit’s Ango Hub provides it.