Product
AI4Animals
Reinventing camera surveillance with AI for better animal welfare and handling
AI4Animals is a smart camera surveillance system that improves how animals are monitored and handled in slaughterhouses. With AI4Animals, slaughterhouses can better identify, address, and avoid welfare and handling issues.
Better animal welfare
Avoid the need to manually sift through hours of footage.
Why AI4Animals?
Surveillance on animal welfare and handling deviations.
Continuous improvement through closed loop reporting and follow up.
Near real-time alerts on serious breaches for quick action.
Learn more about AI4Animals
Utilizing AI technology
Swift intervention is crucial for addressing and avoiding animal handling issues. And unlike humans, our algorithm does not desensitize. Instead, it continuously improves as it learns from new recordings.
Promoting smart animal welfare
Created in collaboration with partners
Over the last years, most major slaughterhouses in the Netherlands have implemented camera monitoring systems. Every day, this results in hundreds of hours of video footage. Although current camera systems can help identify animal handling issues, there are significant limitations. In practice, slaughterhouses review a random selection of the many hours of video footage generated every day. As a result, most video footage remains unseen.
AI4Animals was created in close collaboration with Eyes On Animals, de Dierenbescherming, and Vion. Deloitte developed an AI-based camera surveillance system that monitors how animals are handled in slaughterhouses. AI4Animals was kickstarted and supported by the Deloitte Impact Foundation and Deloitte Innovation. It is developed on Deloitte’s EdgeSense platform, which uses Amazon Web Services (AWS) cloud and edge computing, based on AWS Panorama. As it is cloud-native, AI4Animals can be scaled to multiple locations and processes.
Detecting a wide range of potential welfare issues
The constant interaction between humans and animals in slaughterhouses can lead to a variety of animal welfare deviations. These include:
- Signs of life and consciousness - Detect signs of life or consciousness when the animals should not be alive.
- Animal staying behind – Detect one or more animals staying behind for some time, while others continue. This might indicate lameness, exhaustion, and injuries.
- Mobile stunner usage – Monitor the use of the mobile stunner and whether it has been applied according to protocol.
- Bottlenecks - This occurs when a large group of animals are stuck in the runway, causing stress.
- Human movement - People walking directly against the direction of the pigs can cause stress.
From issue detection to your dashboard
AI4Animals consists of AI models and a dashboard that significantly improves camera surveillance by automatically detecting animal handling issues, in near real-time. It does so in 4 steps:
- AI4Animals uses streaming camera footage and detects animals, people, objects, and how these interact with one another.
- Then a logic layer is applied to the images, that decides whether or not a potential issue has occurred in the video. E.g. how long did the animal stay behind? Was the human movement with or against the stream of the animals?
- It then selects and aggregates all video fragments with potential issues to be reviewed, this happens on-site using edge computing devices.
- The results are reported in trend reports outlining deviations over time and per slaughterhouse.
