The Role of Computer Vision in the Automotive Industry

By Srikanth
7 Min Read
The Role of Computer Vision in the Automotive Industry 1

Despite the timely ascension of the automotive industry, the vertical leaves a lot of scope for incremental improvements. Starting from lowering traffic accidents to improving vehicle manufacturing and resource deployment, Artificial Intelligence seems like the most probable solution to get things moving skywards.


However, AI, as a realm is vast and the automotive industry needs something more granular to grow and fix the existing issues that keep relating to mishaps, driver unfamiliarity, and skepticism concerning autonomy.

The Concept of Training Intelligent Models

For someone looking closely at the automotive sector, empowering cars to detect drivable roads and pedestrians isn’t the only task that Artificial Intelligence looks to undertake. Instead, what the sector needs is a comprehensive approach to train machine learning models to provide better inspection standards while vehicle manufacturing, robotic guidance during driving, defect recognition support during alignment of parts, and more.

As a majority of the automated tasks rely on visualization and the ability of the intelligent systems to detect anomalies in advance, Computer Vision comes forth as an AI subsidiary trusted to make the automotive sector even more responsive and inventive.

What is Computer Vision?

As an AI-powered technology, Computer Vision aims at training requisite models for identifying, detecting, classifying images with contextual accuracy. Intelligent cars and automatic vehicle manufacturing units are progressively being hardwired with Computer Vision setups for processing what’s in front of them, with precision. However, these setups need to be trained with exhaustive data sets for the supervised learning technique to take effect and lead to perfect outcomes.

Also Computer Vision, as a way of training Automotive models, relies on sequenced data annotation strategies like bounding boxes, polylines, semantic segmentation, LiDAR, polygons, and object tracking to ensure that the intelligent setups are capable of preempting everything that is even remotely related to safety, productivity, and quality assurance.

Computer Vision in the Automotive Industry: Scope and Solutions

As mentioned, Computer Vision isn’t only about averting accidents and mishaps. It also entails manufacturing intelligent vehicles to precision, more so with incremental setups installed within. Regardless, the rendezvous of computer vision with the Automotive Industry is all about working out the following possibilities:

  • Automated Manufacturing

Here comes the fun part that most manufacturing warehouses are already implementing to minimize human exposure. This includes using Computer Vision for accurately detecting parts via relevant annotation techniques and empowering robots to pick and drop each, depending on the next course of assembly.

The Role of Computer Vision in the Automotive Industry 2

Computer Vision, in this regard, uses Pattern Recognition support to also identify manufacturing defects, quality of alignment, dimensions, tire assembly, nut threading, and almost anything that involves putting a vehicle together.

  • Quality Assurance

It is important for the manufacturer to be absolutely sure of the vehicle quality, going into the market. Computer Vision empowers firms, via reliable image and video annotation strategies to inspect the safety and electronic components to avoid errors and accidents, in the first place. Also, this technology has a role to play, in ensuring that the General Assembly and assemblage of the Power Train go as envisioned.

  • Intelligent Driving Assistance

When on road, Semantic Segmentation, pertaining to Computer Vision, lets cars make use of automated assistance, by allowing the built-in setup to detect other vehicles, roads, lamps, and pedestrians, for avoiding collisions.

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  • Monitoring System

Computer Vision lays the foundation of highly advanced and segmented driver monitoring systems, capable of identifying facial data of the driver, for responding accurately to gaze estimation and even blink detection.

  • Improved Autonomous Cars

Different image annotation strategies, pertaining to Computer Vision add to the impact and inventiveness of the new breed of autonomous cars, which are expected to get better when it comes to parking safety, sending out perceptive warnings, improved LiDAR orientation for 360-degree analysis of the dynamic scenery, 3D mapping, automatic deployment of airbags, in case of an accident or preemptive threat, lane line identification, low-light driving with HDR and thermal scanners in play, and other aspects.

How the Future Might Unfold?

The continued impressions made by Computer Vision corresponding to the automotive sector will still be questioned further with safety and detection of insignificant obstacles being the causes for concern. However, the next step with AI at the fore should be to implement the various annotation technique extensively to make the vehicle stop at a safe distance and take up contextual choices while entering intersections or lanes.

For smaller objects though, Computer Vision, as a training resource, needs to rely more on Deep learning and Neural networks to make identifications, granular enough. However, challenges will continue to exist as while on road, Computer Vision will also have to make arrangements for identifying defecting lights and remarking unclear lane segregations with AI-friendly signage.

The road seems long for the holistic adoption of Computer Vision on the Automotive vertical but as per the existing statistic, i.e. the $54 billion market share of AVs, the direction taken toward autonomy seems to be the right one.

Contributed by Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.

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