I-AM-Bird: A Deep Learning Approach to Detect Amazonian Bird Species in Residential Environments

Lucas Ferro Zampar, Clay Palmeira Da Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The Amazon presents several challenges, such as recognizing and monitoring its birdlife. It is known that bird records are shared by many bird watchers in citizen science initiatives, including by residents who observe birds feeding at their home feeders. In this context, the work proposed an approach based on deep learning to automatically detect species of Amazonian birds that frequent residential feeders. To this end, a data set consisting of 940 images captured by 3 webcams installed in a residential feeder was collected. In total, 1,836 birds of 5 species were recorded and annotated. Then, we used the dataset to train different configurations of the Faster R-CNN detector. Considering the IoU threshold at 50%, the best model achieved an mAP of 98.33%, an mean precision of 95.96%, and an mean recall of 98.82%. The results also allow us to drive future works to develop a monitoring system for these species in a citizen science initiative.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Agents and Artificial Intelligence
Subtitle of host publicationICAART 2024
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
Number of pages7
ISBN (Print)9789897586804
Publication statusPublished - 24 Feb 2024
Event16th International Conference on Agents and Artificial Intelligence - Precise House Mantegna Roma, Rome, Italy
Duration: 24 Feb 202426 Feb 2024
Conference number: 16

Publication series

NameInternational Conference on Agents and Artificial Intelligence
ISSN (Print)2184-433X


Conference16th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2024
Internet address

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