Artificial Intelligence, in practice, is complex. There are several technologies and training methodologies involved. There is a high risk involved in algorithm failure. With this, businesses want nothing but the best of AI practitioners when it comes to executing the use of AI technology. That’s when the Artificial Intelligence (AI) Center of Excellence (CoE) comes into the picture.
AI Center of Excellence (AI CoE) is a centralized knowledge group or a team that guides and implements AI for organization-wide projects. An AI CoE comprises resources, talent, and knowledge that is required to build AI-enabled projects. It brings all the capabilities under a roof to address the challenges in AI adoption.
An AI CoE is nothing but an internal centralized counsel who identifies the opportunities that AI technologies bring along to resolve various business problems. This may include controlling cost, improving efficiency with automation, optimizing revenue, etc. The key objective of setting up an artificial intelligence CoE is to visualize the business-centric advantages of artificial intelligence and implement them in all AI-related projects.
Introducing Daffodil’s AI Center of Excellence (CoE)
Daffodil Software, a software engineer partner to 100+ organizations across the globe, has been an early adopter of AI technology. The company has extensive experience in the domain which is attested to the successful delivery of AI-related projects. The technology partner has stepped ahead in strengthening its services by setting up AI CoE for its customers.
Our CoE members recently celebrated image segmentation week. The team dedicated the entire week to educating people about this interesting application of AI and its practical use cases.
Image Segmentation is one of several fields in Machine Learning that fall under the category of image data. In this section, a model is trained in such a way that it can identify objects at the pixel level and classify it. Ultimately, the model can accurately extract the objects of interest from the image.
This technology has a variety of applications in different fields, including agriculture, autonomous driving, medical, ariel, water, e-commerce, and more).
Dedicating the entire week to image segmentation and its several applications, Daffodil’s AI CoE researched and found some practical use cases of this technology. Here is an overview of all of them.
NOTE: A practical execution/demo for all these use cases are available to the AI CoE team. You can request a free demo and consultation for any of them by our AI experts.
Use Case: Precision Agriculture
Precision agriculture utilizes modern technology to increase crop quality and profitability while lowering the levels of traditional inputs to grow crops (such as fertilizer, insecticides, water, etc.).
One of the important aspects of precision agriculture is detecting weeds at an early stage, eliminating them, and managing the use of pesticides. By using image segmentation techniques, farmers can separate plants and weeds from an image of a field or garden. This helps them to identify specific species and patterns, enabling farmers to take relevant action as soon as possible.
- Segmenting crops & weeds and providing images with sharp edges is one of the biggest challenges in the process. Although several methods have been proposed to do this, none of them provides sharp and clean images that could differentiate weeds and crops.
- A complex mix of weeds and crops makes segmentation difficult. While separating soil is easy, the detection and segmentation of weeds from crops is comparatively a tough task.
Our Approach: For segmenting weeds and crops from images, semantic segmentation was applied using U-net, followed by segmentation of crops and weeds.
Segmentation of plants and weeds from the image of field/garden.
By Shubhang Malviya
2) Autonomous Driving
Use Case: Autonomous Driving
While autonomous driving is still a future, there have been qualified developments made in the domain. The concept of driverless cars can be utilized in different ways, including:
- Complete automation with a robotic driver (but with extended availability of a driver)
- The robotic driver steers the car from parking to the desired location
- The robotic driver takes over the tasks of the driver exclusively on interstate-like expressways
a) The image segmentation model takes high computation even when they are trained to provide predictions.
b) AI algorithms, especially deep learning require special hardware due to the amount of data and the complexity of computations to process.
c) Optimizing the hardware, graphic processing units (GPU), and tensor processing units (TPU) need to be optimized for a fast computation cycle.
Our Approach: Semantic segmentation was applied using inceptionv3 with transfer learning. This was followed by segmentation of cars, trucks, and other 17 categories on AWS Sagemaker for leveraging high training speed.
Segmentation of vehicles, humans, roads/lanes, road signs, traffic lights, etc.
By Danish Bansal
Use Case: 3D medical imaging
The process of extracting regions of interest from 3D image data, such as that from MRI or CT scans, is known as medical image segmentation. The major objective of segmenting this data is to find the parts of the anatomy needed for a specific investigation, like extracting malignancies from a brain MRI. This is a difficult and time-consuming task.
Medical image segmentation is used in the diagnosis of tissue abnormalities. However, there are times when the abnormalities go unnoticed by the naked eye. For example, a small spot in the MRI can be skipped by a doctor but it can be caught by image segmentation and model training. Moreover, it is difficult to diagnose and locate tissue abnormalities in multiple images altogether.
Our Approach: We utilized the power of 3D U-Net for performing volumetric segmentation of MRI images.
3D Medical imaging and their pre-processing
By Saksham Thukral
4) Drone Imagery
Use Case: Segmentation of water bodies, buildings, roads, and other objects
For example, if an area is affected by a natural disaster, it is not possible to analyze the loss done by visiting the place. Instead, if images of the affected are available, it can help to assess areas after natural disasters.
Using satellite imagery, we can identify which area has water, buildings, roads, and other important features. However, the challenge with Satellite images is that they are large in size and shape, making them difficult to process.
If satellite imagery analysis has to be replaced with image segmentation, then lack of data is one of the biggest challenges there. There are only a few ariel images out there that could be utilized for training data with high accuracy.
Our Approach: Semantic segmentation was applied to distinguish between buildings, land, water, vegetation, etc.
Segmentation of water bodies, buildings, roads, and other objects
By Deepanshu Tyagi
5) Geospatial Imagery
Use Case: Segmentation of water bodies from an image
Hurricanes and urban flooding can pose serious hazards to the people who live in harm’s way. Separating water from Satellite Images of Water Bodies enables water to minimize its destruction.
Challenges: Small objects, fuzzy edges, low-light intensity, texture or color clustering, etc.
Our Approach: Semantic segmentation was applied followed by U-net. To improve prediction for images with fuzzy edges, the smooth blending technique was adopted.
Segmentation of water bodies from an image.
By Amit Kumar
AI Projects Delivered under Center of Excellence
Daffodil Software is extensively working on AI-powered software projects. Here is a list of varied AI-based solutions in diverse industries that are developed under the guidance of the COE team.
- Leveraging AI to detect green space from geospatial data (Read more)
- Smart monitoring and anomaly detection system for oil & gas turbomachinery (Read more)
- Data management solution to remove redundancy from data points (Read more)