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Computer vision and its hardware market are expected to be a $48.6 billion industry by 2020. What started as an experiment in the 1950s has emerged as a new market and all the credit goes to the advances in deep learning and digitization of data coupled by incessantly increasing computational power.  

As a branch of artificial intelligence, computer vision uses photos, videos, and thermographic images to train the dedicated algorithms to enhance the accuracy rates of object identification. Emulating human vision required machines to acquire, process, analyze and understand the data from the images. As the deep learning neural networks that are capable of iterative learning got better, training and inferencing stages of computer vision gained accuracy.

Over a decade the computer vision systems progressed from being 50% to 99% accurate. The reaction span of computer vision algorithms to visual inputs and changes has improved dramatically which establishes its use in real-time systems across industries. The automotive, agriculture, pharmaceutical, energy, packaging, linguistics, retail, banking, and other sectors.

In this article, we present the 5 major sectors where the progress has been dominated by computer vision and its trends. An exhaustive review of the industries establishes the kind of accomplishments computer vision has achieved.

Computer Vision in Precision Agriculture

Agriculture was among the pioneering sectors that adopted computer vision. Over the decades of innovation, computer vision has been a major contributor to precision agriculture. Multi-temporal remote sensing imagery processing, hyperspectral reflectance imaging, and soil analysis technologies are just the tip of the ice-berg of how machine vision is aiding in yield mapping, estimation, arable farming and a lot more.

Field Robots powered by machine vision are being deployed to automate the manual tasks of harvesting, planting, and weeding. Models like Bayesian Classifier is being used for Statistical and Syntactic Pattern Recognition for optimizing the use of manual labor. The hyperspectral imaging system is being utilized for phenotyping to ensure the growth of best crop breeds is propagated. For livestock identification, Support Vector Machines are being used to identify and monitor livestock.

Machine vision has become an integral part of precision agriculture and opening new horizons of agricultural efficiency and profitability.

Computer Vision in the Automotive Industry

Right from autonomous vehicles, price-quality wars, and assembly automation, Computer vision finds a multi-faceted application in the automotive industry.  For self-driving cars, stereo, and motion analysis, domain adaptation techniques, and more such models are used for image segmentation.  

To state an example, for avoiding a collision in autonomous vehicles, indistinguishable features are extracted from source and targets which are then reconstructed and mapped to make sure the collision is avoided. LiDAR(Light Detection and Ranging) systems are used to illuminate the target to detect obstacles and lead to safe self-driving.

With the technological advances in image classification and localization using Convolutional neural networks (CNN) and object detection using R-FCNs and SSDs, the safe self-driving cars are our future.  

Computer Vision in Healthcare

In healthcare, computer vision has found profound and diverse use cases. Utilizing the models of image and pattern recognition, the 3D pattern of tumors are built so that the disease can be targeted in a more profound manner. It requires computer vision models to go beyond Euclidean data and develop Geometric Deep Learning (GDL).

Such models with the support of hardware have established some biggest breakthroughs. Identification of cancerous cells from the biopsy reports and recognizing skin tumors more quickly than dermatologists are the biggest leaps for healthcare.

Utilization of gamification and motion sending to detect strokes or brain abnormalities promises identification of diseases like Alzheimer’s before they aggravate. Deep learning algorithms are trained on the basis of specialized imaging techniques to track minute changes in the brain cells which have remained a challenge for the doctors and researchers over the decades.

Computer Vision for Security System  

As the precision of Computer vision widened so did its applications in security and surveillance systems. Computer vision systems are finding its place in cargo inspection, embassy and military surveillance, hospitals, parking lots, airports and more such public places.

Acquiring surveillance data is no more a challenge but identifying, processing and reconstructing requires various models of computer vision. For example, models like the Hidden Markov Model, Visual Hull Technique, stereo vision, iso-luminance, and 3D template matching is used for Giat recognition.

Conclusions

Research in Computer vision is aggressively changing industry operations. Impressive hardware advancement and innovative solutions are improving the world. Trends like synthetic data, visual question answering, domain adaptation, generative adversarial networks, and 3D object understanding have been leading to the explosion of computer vision. The utilization is leading to precision in healthcare and agriculture, cost-effectiveness in manufacturing and packaging. This multimodal information collection, processing and implications are making industries make substantial progress. Some industries have been rapidly adapting to computer vision implementation while others are waiting at the edge. As technology continues to evolve and fine-tune, very soon we will see computer vision everywhere. 

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