The emergence of new machine learning and computer vision methods has enabled the development of new solutions for industrial inspection. In this blog, we discuss the automatic detection of objects in industrial images for the purpose of process monitoring and quality assurance. We introduce a fully convolutional neural network (CNN) framework to perform robust image classification at high speed on CPU or GPU while reducing storage footprint by up to 80%. A CNN-based framework is able to outperform the state-of-the-art methods in industrial image classification.
The CNN framework can be used to classify industrial images with high accuracy and low latency. The deep learning-based method for the detection of industrial objects in thermal images, which can be used to monitor temperature levels in industrial processes.
In the past decade, there have been a number of developments in the field of Object Detection. There are five main uses for this technology: quality management, sorting and packaging, inventory management, supply chain tracking and localization, and miscellaneous use cases. As we go through each use case, we will be able to see how Object Detection could be applied to newer manufacturing practices
The quality control process depends on human visual understanding and quick adaptation. However, the AI can automatically distinguish whether a part on an assembly line is good or faulty in seconds. This allows you to take corrective action quickly, which means you will have time to fix the problem before it becomes a bigger issue, saving precious time that could be dedicated to other tasks in dynamic environments.
The AI’s performance will increase considerably over time and be able to learn from experience. The introduction of artificial intelligence in quality control processes is a trend that will continue to grow in the coming years, with many businesses already utilizing AI from product quality monitoring to increase productivity and cut costs.
Sorting is a lengthy and costly process, especially when it comes to manually sort items. The sorting of objects can be made more efficient through the use of AI-powered Object Tracking, which allows for specific parameters to be selected and the number of objects displayed to be counted. This makes for a more flexible assembly line and reduces the number of abnormalities during categorization.
This technology is currently being used in factories and warehouses, with a number of businesses utilizing the system to track the movement of goods. The AI allows for an operator to select certain parameters, such as size or color, and then view all the products that match those parameters.
Autonomous data collection has been a driving force in the development of artificial intelligence. AI helps companies in several ways: by identifying patterns and trends, by being able to store massive amounts of data that humans would never be able to process.
In the case of Inventory management, AI can be used to track items in real-time and make sure products are being counted correctly. This allows for more accurate calculations and prevents wasted time. The automation of this task by AI reduces the risk of human error, allowing inventory to be counted accurately and efficiently. AI will help businesses reduce costs by managing inventory and ordering the right quantity of products to ensure that no money is wasted at the same time increase profits.
The assembly line in the manufacturing industry is almost completely automated. However, this can be improved with the use of AI technology. The use of object detection allows for better labor and higher output. This is done by using sensors to detect the position of the object and moving it accordingly.
AI is used to do what humans cannot do. For example, if a human worker has to place a part in a specific location, they might not be able to do it with precision. Whereas, an AI-powered object detection opens the doors towards this possibility giving modern assembly lines more flexibility.
Custom Object Detection
With the rise of 3D printing and object detection, manufacturing industries can be made more efficient. Objects take a variety of forms and usually algorithms need thousands of training examples to learn to differentiate the products. With this technology programmers are able to use less than 50 of these examples to train the algorithm to perform with accuracy and efficiency. The machine learning algorithm of the future will be able to learn from a set of 20 objects or less.
In the last decade, new technology has enabled a significant leap forward in AI. As cameras and sensors improve, Artificial intelligence is better able to make sense of their surroundings and identify objects in them. In order for robots to truly work alongside humans in a factory setting, they must be able to interact with their environment. This requires an understanding of computer vision. Datamoo.ai provides that understanding and helps you to choose the right technology needed for your organization.