# Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals

arXiv: https://arxiv.org/abs/2606.10940 · HTML: https://mlsanity.com/paper/2606.10940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection](https://mlsanity.com/paper/2509.25164/) (2025)
- [A deep active learning system for species identification and counting in camera trap images](https://mlsanity.com/paper/1910.09716/) (2019)
- [Deep Learning Object Detection Methods for Ecological Camera Trap Data](https://mlsanity.com/paper/1803.10842/) (2018)
- [Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation](https://mlsanity.com/paper/2405.12930/) (2024)
- [Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology](https://mlsanity.com/paper/1910.07360/) (2019)
- [Harnessing Artificial Intelligence for Wildlife Conservation](https://mlsanity.com/paper/2409.10523/) (2024)
- [Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning](https://mlsanity.com/paper/2305.02097/) (2023)
- [Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps](https://mlsanity.com/paper/2304.12703/) (2023)
- [Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data](https://mlsanity.com/paper/2411.14219/) (2024)
- [Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time](https://mlsanity.com/paper/2603.20509/) (2026)
- [Long-tailed Species Recognition in the NACTI Wildlife Dataset](https://mlsanity.com/paper/2510.21657/) (2025)
- [AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10](https://mlsanity.com/paper/2411.15263/) (2024)

## Abstract (arXiv)

Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles. In an attempt to remove barriers and increase uptake, we release an open-source object detection model for 31 classes, 28 common UK mammal and bird species, plus utility classes for humans, calibration poles, and vehicles, drawn from a curated dataset of 48,165 labelled instances assembled from multiple sites over a decade of operational deployment through Conservation AI and its successor, Trap Tracker. The model, a YOLO26x detector trained and tested on an 80/10/10 class-stratified split, achieves a mean Average Precision of 0.984 at Intersection over Union (IoU) of 0.5 (0.956 at IoU 0.5-0.95) on the held-out validation set, with precision 0.988 and recall 0.965. On an unseen held-out test split, mean per-species confidence ranged from 0.96 to 0.99 across the 31 classes, with a 0.17% false-negative rate concentrated in difficult night-time, distant, or occluded images. These metrics are from data from the same pool of sites and cameras as training, so performance at entirely new sites is left to future work. We release the trained weights in ONNX format under a non-commercial licence, with local desktop and real-time camera support, aimed explicitly at ecologists with no machine-learning experience. This release is a deliberate counterweight to the multiple paid for models that have developed over the last decade.