World over there are many government agencies that are deploying artificial intelligence in image recognition for various purposes. There are many purposes and each one of them are enticing use case scenarios. Talking of which Artificial Intelligence in Automotive, Banking Financial Service and Insurance, Healthcare, Security, and Retail have the most compelling demand to which this topic would be focused on primarily.
Talking about artificial intelligence in image recognition in the automobile industry, self-driving cars would be a rage when talked about 4 decades ago. But come 2019, this has become a reality, still progressing and in the development phase. Currently, there are prototypes that are being tested by Google, Tesla, Apple, Ford, and General Motors. There have been heavy investments by these companies on self-driving cars and the value has reached ~USD 7.25 billion.
There are various levels of automation that the vehicles may have achieved and based on these levels there is further segmentation:
Driver assistance: this includes safety features which are mandatory by regulators across various countries.
Partial automation: this is the level 2 automation which includes blind-spot detection, stability control, and collision warning while keeping the driver engaged simultaneously.
Conditional automation: it is like an autopilot in an airplane. The driver only needs to supervise the vehicle. This is a level 3 automation.
High automation: this includes lane-keeping, traffic jam assistance, and self-parking. This is a level 4 automation in the vehicle.
Full automation: This is a level 5 automation, total from the future. No need for a driver and the vehicles communicate with each other through integrated Artificial Intelligence and Machine Learning through the Internet of Things.
The second eye for Automotive: Computer Vision
The driver’s mistake can be compensated if there is a second eye installed in the car which can avoid human fatal errors. Computers don’t go through human fatigue in any case and they can be only avoided through computer sensed processed algorithms. Computer vision in the automobile can mimic human behavior through machine learning and can avoid certain errors through constant learning.
In the primary step, the computer vision processing unit identifies the objects with a machine learning algorithm integrated through the cloud computing (typically a convolutional network), this algorithm has been trained on millions of images from the real-life environment. At this point, the computer assigns tags to each object, like “a car,” “a pedestrian,” “a traffic light,” “street furniture,” or “a cat” and determines their geometric boundaries.
The problem that could arise is that convolutional networks only know how to classify single objects this can be disastrous in real-world instances. This problem is solved by moving a sliding window over the image and breaking it into smaller images. The image gets split into a grid, and each piece of the grid receives a score regarding the object it holds.
The next step is about making predictions about the previously identified objects. For instance, the distance of cars traveling and their proximity being calculated for any sort of accidents? Are the pedestrians on the sidewalk or crossing the street? This detection is done through image localization. The difficulty here is that the same object might be split across multiple grid cells, a challenge solved by identifying the cells with the highest probability of having a specific object in adjacent cells.
This was Artificial intelligence in Image recognition only in the automotive sector, there are numerous possibilities and use case scenarios of this technology. The technology itself is at a very nascent stage and it may take quite some time to get to the mainstream market for commercial use.
