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Businesses and governments are using computer vision to make waste management safer and more efficient. AI models can sort waste quickly, handle hazardous waste, sort waste based on markings and even identify waste items from the air. However, these important use cases can be subject to bias. Training data that

Computer vision models are being used to streamline processes in rapidly changing industries. For example, the demands of COVID-19 have led to computer vision being used by airports and security services. And our changing economy is leading to computer vision innovation in other surprising ways. Collectibles, such as sports memorabilia,

Computer vision based AI projects are heavily reliant on high quality image and video data. In order to train the next generation of AI models accurately annotated image and video datasets must be assembled at the scales that developers require. But before the annotation process can begin companies need to

Computer vision is creating revolutionary smartphone applications. Computer vision based AI models are able to understand digital images and video and recognise real objects in the world around them. As a result they can help smartphone users to take pictures, find things, see the world in a new way, and

Effective security systems make people feel safer. As a result businesses and public sector organisations are constantly looking to improve the security of their spaces. However, investing in security staff can be expensive, and proper security training can be a lengthy process. In addition it can be difficult for even

AI is making processes easier in a wide variety of industries. In the manufacturing sector smart AI systems can make production lines more efficient and safer. Similarly, in the waste management industry computer vision based AI models can sort waste autonomously. In addition to these highly visible advances AI has

Quality image and video data is at the heart of any successful computer vision based AI project. Precisely annotated training datasets invariably lead to models which are capable of high-level performance in a chaotic and changing world. The inverse is also true, datasets strewn with errors and inaccuracies can lead