It is expected that advancements in robotic surgery will continue to grow. Some experts predict that robotic surgery will become the standard of care for some procedures. Among other sensors, the system uses advanced computer vision, allowing surgeons to “feel” what they do during the procedure.

Artificial intelligence algorithms fed by specialised cameras will continue integrating into the robotic systems, allowing for more autonomous and precise operations. Virtual and augmented reality advancements will enhance the surgeon’s ability to visualise and manipulate surgical sites. It will also benefit the training before performing actual surgeries.

There is a trend involving the use of deep neural networks to help us understand the functions of the human body. Increasingly sophisticated computer vision methods help to segment blood vessels, identify stroke aetiology, or detect malignant tumours. Complex radiology assistant tools in development are based on advanced computer vision methods.

By leveraging computer vision techniques, doctors can analyse health and fitness to assist patients in making better medical decisions. In contemporary medicine, doctors use it to gauge blood loss during surgeries, particularly C-sections. It can help take emergency measures more effectively when the quantity of lost blood reaches the last stage. Additionally, this technology can measure the body fat percentage in people using the images captured by regular cameras. The pose analysis algorithm can aid rehabilitation processes and detect postural defects.

Installing a fixed camera in an animal stall allows the owner to monitor the livestock remotely. Image processing methods enable automatic anomaly detection like events or mass panic. Computer vision can also aid in ensuring proper animal growth. Instance segmentation and pattern recognition methods allow the automatic tracking of individual animals.

A drone with a camera can help a farmer with monitoring crop growth. Deep learning-based detectors can analyse video data, raise alerts on possible losses, and calculate the waste percentage. Automated monitoring systems can help increase security by automatically detecting intruders or thieves.

Decision-making systems trained on crop datasets can determine an optimal time window for harvest based on farmland images. A route-planning system with visual aids can aid in managing the fleet, which can save fuel.

Applying computer vision technology can automatically sort seeds for maximised yield by discarding potentially flawed sprouts. The exact process can be used to select fodder for the livestock and help alleviate health issues caused by spoilt supplies. A computer vision model can also be used to evaluate the uses of deceased animals.

Inspecting harvests can help detect diseases affecting crops and help find a solution to counteract them. Image-supported sorting stands can save manual labour by visually inspecting and automatically separating flawed plants.

Evolving facial recognition and analysis algorithms like emotion recognition, gaze estimation or expression recognition can take human-robot interactions to the next level. With more human-like behaviour, humanoid robots will be able to break the barrier of distrust of machines. CV can give robots reliability, making them an integral part of everyday life.

For most mobile robots and drones, cameras will replace most currently used sensors. Thanks to computer vision projects, those robots could function with the highest level of autonomy thanks to the intense work of automation experts.

Technological processes are another promising field for adopting computer vision projects. Extensive work on simplifying and fine-tuning deep learning models and algorithms can help introduce CV techniques and enable use by industry workers without specialised domain knowledge.

The paradigm of meta-learning can be beneficial in this case, as it tries to mimic the human approach to deep learning model for new tasks by drawing on previous knowledge. Additionally, computer vision algorithms can aid in maintaining the safety of workers, leading to the popularisation of cooperative robots.

Assistant Professor at Gdańsk University of Technology, AI/ML Expert at M5 Technology, Member of the Polish Society For Measurement, Automatic Control And Robotics. works on the company’s research agenda and works on the implementation of both EU-funded and commercial R&D projects from the field of Computer Vision, Machine Learning and Embedded Systems.

Involved in projects related to computer vision, sports applications, medical diagnosis, and recommender systems. He graduated from the Computer Science degree in at Gdansk University of Technology. He has a MSc in Investment Management, a PhD in Finance, and a PgCert in Academic Practice in Higher Education at Coventry University.

Seasoned engineer with many years of experience in international tech companies. Currently holds a team leader role in gaze estimation projects developed in the company.

A Computer Vision Engineer with experience in agriculture, fast food, sports analytics, and healthcare. He loves researching SOTA and converting it into an MVP in Pytorch. However, he recently delved deeper into MLops.

Graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in applying synthetic datasets for learning deep neural networks and in learning algorithms to reduce the amount of data required for effective network training.

Currently enrolled in an industrial PhD programme at the Gdansk University of Technology. His main interests are remote sensing (processing done specifically on satellites), machine learning algorithms for edge devices, and parallel computing.

Graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in using deep learning in biomedical engineering and generating synthetic data such as photos and texts

Graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. His areas of interest mainly focus on computer vision tasks, including biomedical data processing and deep neural network training and evaluation. Apart from that, he also enjoys web development topics