Semantic segmentation of mammals in forest regions with U-NET

Environmental monitoring is vital for preserving animal biodiversity, providing researchers with essential metrics on environmental health and aiding efforts to combat extinction. Mapping the fauna of forest regions is, therefore, essential for informed decision-making and effective planning. However, manual mapping is time-consuming and labor-intensive for biologists. To address this, our study utilized Deep Learning techniques to assist in detecting mammal species in the forests of northwestern Paraná, using videos provided by NAPI Biodiversidade. We implemented a semantic segmentation algorithm using the U-NET convolutional neural network to segment forest images. Our experiments demonstrated that after histogram equalization and without forest grouping, the Dice Coefficient was 79.40%, and the F1 Macro Score for animal presence classification was 86%. However, with forest grouping, there was a substantial drop in the model’s performance, highlighting difficulties in generalizing to new locations. This suggests the necessity of training location-specific models. Additionally, applying histogram equalization through contrast stretching improved the quality of the generated masks and classifications. However, the presence of false negatives indicates a need for more training data.