Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Many enterprise customers want to identify missing components in products, damage to vehicles or structures, irregularities in production lines, minuscule defects in silicon wafers, and other similar problems. Amazon Lookout for Vision uses ML to see and understand images from any camera as a person would, but with an even higher degree of accuracy and at a much larger scale. Amazon Lookout for Vision eliminates the need for costly and inconsistent manual inspection, while improving quality control, defect and damage assessment, and compliance. In minutes, you can begin using Amazon Lookout for Vision to automate inspection of images and objects--with no ML expertise required.
With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy with anomaly detection, help provide continuous improvements, and retrain models with updated predictions. However, you're often forced to choose between an ML-only or human-only system. Companies are looking for the best of both worlds, integrating ML systems into your workflow while keeping a human eye on the results to achieve higher precision.
Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision. You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3, Amazon SQS, AWS Lambda, AWS Step Functions, and Amazon SageMaker.
Improving radiologist efficiency and preventing burnout is a primary goal for healthcare providers. A nationwide study published in Mayo Clinic Proceedings in 2015 showed radiologist burnout percentage at a concerning 61% . In additon, the report concludes that "burnout and satisfaction with work-life balance in US physicians worsened from 2011 to 2014. More than half of US physicians are now experiencing professional burnout." As technologists, we're looking for ways to put new and innovative solutions in the hands of physicians to make them more efficient, reduce burnout, and improve care quality.
According to the 2018 National Highway Traffic Safety Administration (NHTSA) Traffic Safety Facts, in 2018, there were 857 fatal bicycle and motor vehicle crashes and an additional estimated 47,000 cycling injuries in the US . While motorists often accuse cyclists of being the cause of bike-car accidents, the analysis shows that this is not the case. The most common type of crash involved a motorist entering an intersection controlled by a stop sign or red light and either failing to stop properly or proceeding before it was safe to do so. The second most common crash type involved a motorist overtaking a cyclist unsafely. In fact, cyclists are the cause of less than 10% of bike-car accidents.