Deep learning is a machine learning subset that uses layers of neural networks to simulate the decision-making capabilities of human brains. Some forward-thinking parties have explored options for applying it to machine vision to enhance results. Machine vision applications – which allow algorithms to extract information from videos, images and other content – have become popular for meeting needs related to quality control, navigation security and more. How does deep learning improve the results?
1. Making Drones More Functional in Challenging Environments
Drones can increase users’ visibility in environments from sprawling warehouses to natural disaster zones. People often deploy them when they need additional details to determine if it is safe enough to send crews to particular areas with known risks. Machine vision enables these autonomous crafts to take off, land and navigate safely.
However, a recent, relevant example highlighted how deep learning can broaden potential applications. It concerned an autonomous helicopter, which, thanks to the deep-learning algorithms, could safely lift off, touch down and navigate its environment. Tests also indicated that this application worked well in settings with poor GPS coverage.
Those involved with this project believe it will increase the applicability of drones and similar airborne vehicles that can fly themselves. Such benefits could broaden how businesses and other entities deploy drones in circumstances that are too dangerous or cumbersome for humans.
Additionally, drones can reach locations by air that are too risky for people to travel to through land-based means due to insufficient infrastructure or other challenges. That capability reinforces safety while capitalizing on efficiency.
Well-trained algorithms are excellent at analyzing vast quantities of data, and built-in processors enable drones to support that process with little or no information transmission required. Such equipment enables timely analyses for tasks such as wildlife tracking or traffic monitoring. These technological applications gather the necessary information without subjecting humans to unnecessary risks or forcing them to spend their time on potentially unproductive activities that drones might tackle faster and more accurately.
2. Meeting the Needs of Busy Industrial Environments
Many manufacturers use machine vision tools to meet quality control needs, especially since these options allow nondestructive testing, supporting companies’ reputations for providing reliable goods.
Some machine vision tools can scrutinize items in seconds, assessing them to determine if they meet or fall short of a brand’s standards. Then, workers can analyze those items that failed the checks to learn if the products are part of a more extensive issue that needs immediate attention. These collaborative efforts between people and advanced machines keep operations running smoothly, increasing factories’ potential output.
Statistics indicate that companies applying advanced solutions have achieved up to 12% improvements in production, productivity and factory utilization. Additionally, advanced artificial intelligence – including deep learning – can adapt to changing needs and learn through ongoing use. Since their performance improves over time, the algorithms gradually become better at flagging hard-to-detect or complex anomalies.
3. Detecting Infrastructure Dangers With Autonomous Robots
Many experts have warned that the poor condition of roads and bridges in the United States is a pressing matter requiring urgent attention. However, infrastructure inspections are time-consuming and require significant labor. One engineering research team aimed to address this issue by equipping autonomous robots with deep-learning algorithms and machine vision.
More specifically, the group prioritized a proactive approach that screens failure-prone areas with machine-vision components. The gathered images create a “digital twin” that infrastructure specialists can use to examine issues and how they change over time. That information can shape their decisions about which repairs to handle first and categorize abnormalities based on severity.
The researchers realized that the poor condition of much of the nation’s infrastructure makes setting up a triage system the best first action. That’s particularly true since many potentially dangerous flaws are tiny and hard for humans to detect without help. Fortunately, combining machine vision and deep learning could provide highly accurate, efficient assessments that enable people to send crews to the most pressing issues.
People apply similar time-based strategies to machine vision applications to examine how patients’ tumors change or whether athletes’ performance improves during a season. Such efforts can be extremely valuable, especially for helping the concerned parties respond to unwanted developments sooner.
Deep Learning and Machine Vision: A Powerful Pairing
Machine vision technologies are highly capable and increasingly used in settings such as industrial factories. However, the examples here show how people can optimize their results by exploring suitable opportunities to apply deep learning strategically. Training those algorithms on high-quality data for specific use cases can help people increase efficiency and consistently achieve better accuracy in demanding applications.
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