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

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.