What is image recognition?
In the context of ML, image recognition is the ability of software to identify objects, people, places, actions, and writing in images. Computers can use machine vision technologies combined with a camera and AI software to achieve image recognition. It is used to perform many machine-based visual tasks, such as labeling the content of images with meta-tags, performing image content search, and guiding autonomous robots, self-driving cars, and accident-avoidance systems.
What are the uses of image recognition?
- Drones: Drones equipped with image recognition capabilities can provide vision-based automatic monitoring, inspection, and control of the assets located in remote areas.
- Manufacturing:Inspecting production lines, evaluating critical points regularly within the premises. Monitoring the quality of the final products to reduce the defects.
- Autonomous Vehicles: Autonomous vehicles with image recognition can identify activities on the road and take necessary actions. Mini robots can help logistics industries locate and transfer objects from one place to another.
- Military Surveillance:Detection of unusual activities in the border areas and automatic decision-making capabilities can help prevent infiltration and save soldiers’ lives.
- Forest Activities:Unmanned aerial vehicles can monitor the forest, predict changes that can result in forest fires, and prevent poaching. It can also provide complete monitoring of the vast lands which humans cannot access easily.
What are the challenges of image recognition?
- Viewpoint Variation: The systems predict inaccurate values by the images fed to the system with image entities aligned in different directions.
- Scale Variation: Variation in size affects the classification of the object. The closer you view the object, the bigger it looks in size and vice versa.
- Deformation: Objects do not change even when they are deformed. The system learns from the perfect image and perceives that a particular object can only be in a specific shape.
- Inter-Class Variation: Certain objects vary within the class. They can be of different shapes and sizes but still represent the same class.
- Occlusion: Certain objects obstruct the full view of an image and result in incomplete information being fed to the system. It is necessary to devise an algorithm that is sensitive to these variations and consists of a wide range of data samples.