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KS update 1.4
== Self Driving Cars ==
== Self Driving Cars ==
Autonomous Cars use a combination of Computer Vision and LiDAR to detect pedestrians and Stop Signs when driving passengers.
Autonomous Cars use a combination of Computer Vision and LiDAR to detect pedestrians and Stop Signs when driving passengers.
== Infrastructure Inspection ==
Checking big structures like bridges, highways, and tunnels for damage is very difficult and dangerous for humans. Today, drones and vehicles with cameras can take thousands of photos of these structures. Computer vision then automatically looks for tiny cracks, rust, or potholes that people might miss. This helps fix problems early before they become dangerous.<ref>{{Cite web|title=Drones improve surveying on Indianapolis highway project|url=https://www.asce.org/publications-and-news/civil-engineering-source/civil-engineering-magazine/article/2022/07/drones-improve-surveying-on-indianapolis-highway-project|access-date=2026-02-12|website=www.asce.org|language=en-US}}</ref>
== Workplace Safety ==
In factories and on construction sites, computer vision is used to make sure everyone stays safe. The system can watch camera feeds to check if workers are wearing their safety gear, like hard hats (helmets) and high-visibility vests. If someone enters a dangerous area without the right gear, the computer can send an alert immediately to prevent injuries.<ref>{{Cite web|last=Bigelow|first=Sam|date=2024-12-09|title=The Role of AI in Construction Safety: Enhancing Workplace Practices|url=https://getmojo.ai/blog/the-role-of-ai-in-construction-safety/|access-date=2026-02-12|website=Mojo AI|language=en-US}}</ref>
== Computer Vision Media ==
== Computer Vision Media ==
File:Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks.png|Learning 3D shapes has been a challenging task in computer vision. Recent advances in [[deep learning]] have enabled researchers to build models that are able to generate and reconstruct 3D shapes from single or multi-view [[depth map]]s or silhouettes seamlessly and efficiently.
File:Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks.png|Learning 3D shapes has been a challenging task in computer vision. Recent advances in [[deep learning]] have enabled researchers to build models that are able to generate and reconstruct 3D shapes from single or multi-view [[depth map]]s or silhouettes seamlessly and efficiently.
File:DARPA Visual Media Reasoning Concept Video.ogv|[[DARPA]]'s Visual Media Reasoning concept video
File:DARPA Visual Media Reasoning Concept Video.webm|[[DARPA]]'s Visual Media Reasoning concept video
File:Mars Science Laboratory, 2011-Present.jpg|Artist's concept of ''[[Curiosity (rover)|Curiosity]]'', an example of an uncrewed land-based vehicle. The [[stereo camera]] is mounted on top of the rover.
File:Mars Science Laboratory, 2011-Present.jpg|Artist's concept of ''[[Curiosity (rover)|Curiosity]]'', an example of an uncrewed land-based vehicle. The [[stereo camera]] is mounted on top of the rover.