Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection.

Visual Inspection is routinely carried out across industry to determine whether a structure, product, component or process meets the specified requirements. Typical examples include the detection of product defects in-service or during maintenance and as point of manufacturing in-process monitoring. Such inspection is usually carried out by a trained individual who has sufficient knowledge and experience to visually identify faults and non-conformant quality and performance. Artificial Intelligence (AI) provides an opportunity to introduce innovation and new technology to the visual inspection process, offering a solution to challenges and requirements. The Centre for Modelling & Simulation (CFMS) has produced a demonstrator that uses a combination of computer vision and AI technologies to automate the manual inspection process.

Ask any technician who has ever scaled a massive wind turbine and they'll tell you: Manual inspections of industrial assets can be dangerous and imprecise. Today, companies are tapping advanced tech tools like autonomous robots and drones, as well as AI-powered predictive analytics, to make inspections not only safer but more accurate. Avitas Systems, launched in 2017 by GE Ventures, is one of the emerging pioneers who are upending traditional modes of industrial inspection. The Boston-based company combines autonomous robot and drone inspections, AI analytics and digital data warehousing in a single service. Not yet two years old, Avitas Systems already counts heavy hitters in the oil and gas, electric power and transportation industries among its client roster.

General Electric (GE) was born when several electrical companies owned by Thomas Edison were merged under a single name - Edison General Electric Company - in 1889. Fast forward to 1892 when Edison General Electric Company merged with Thomson-Houston Electric Company, and both became united under a single name - General Electric. Today, GE is a multinational conglomerate corporation incorporated in New York with its headquarters in Boston, Massachusetts. The company has hundreds of interests, which cater to the needs of the financial services, medical devices, life sciences, pharmaceutical, automotive, software development and engineering industries. GE has revenues of $126,661 million, which places it at #13 on the Fortune 500 list.

Technologies like artificial intelligence and deep learning are driving the evolution of drones and fueling their autonomous future, according to Jesse Clayton, senior manager of product management for intelligent machines at Nvidia. Clayton spoke with SearchCIO at the recent InterDrone conference in Las Vegas, where he discussed the underlying technologies that are shaping the future of drones. In this video, he gives an overview of the commercial applications of drones and explains how advances in AI are impacting the drone industry. What are some of the most surprising business applications of drones that you have seen? Jesse Clayton: Before we talk about the applications, it's important to understand some of the big trends that are happening in technology right now.

Avitas Systems, a GE subsidiary based in Boston, is now using drones and robots to automate the inspection of infrastructure such as pipelines, power lines, and transportation systems. The company is using off-the-shelf machine-learning technology from Nvidia (50 Smartest Companies 2017) to guide the checkups, and to automatically identify anomalies in the data collected. The effort shows how low-cost drones and robotic systems--combined with rapid advances in machine learning--are making it possible to automate whole sectors of low-skill work. While there is plenty of worry about the automation of jobs in manufacturing and offices, routine security and safety inspections may be one of the first big areas to be undermined by advances in AI. Drones have been used on some industrial sites for a while (see "New Boss on Construction Sites Is a Drone"), and various companies, such as Kespry, Flyability, and CyPhy, offer aerial systems for monitoring mines, inspecting wind turbines, and assessing building insurance claims.