ReFrogID: Pattern Recognition for Pool Frog Identification Using Deep Learning and Feature Matching

The global decline in amphibian populations is a pressing issue, with numerous species facing the threat of extinction. One such species is the pool frog, Pelophylax lessonae, whose Norwegian population has experienced a significant long-term decline since monitoring began in 1996. This decline has pushed the species to the verge of local extinction. A substantial knowledge gap in the species' biology hinders the proposal and evaluation of effective management actions. Consequently, there is a pressing need for efficient techniques to gather data on population size and composition. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have shown promising results in various domains, including ecology and evolution. Current research in these fields primarily focuses on species modeling, behavior detection, and identity recognition. The progress in mobile technology, ML, and DL has equipped researchers across numerous disciplines, including ecology, with innovative data collection methods for building knowledge bases on species and ecosystems. This study addresses the need for systematic field data collection for monitoring endangered species like the pool frog by employing deep learning and image processing techniques. In this research, a multidisciplinary team developed a technique, termed ReFrogID, to identify individual frogs using their unique abdominal patterns. Utilizing RGB images, the system operates on two main principles: (1) a DL algorithm for automatic segmentation achieving AP@89.147, AP50@99.123, and AP75@98.942, and (2) pattern matching via local feature detection and matching methods. A new dataset, pelophylax lessonae, addresses the identity recognition problem in pool frogs. The effectiveness of ReFrogIDis validated by its ability to identify frogs even when human experts fail. Source code is available at here.