Bats, Spectrograms, and Deep Learning: Unmasking the Secrets of Mining Area Caves - Gonsales, Arthur; Santos, Vitor C. A.; Appel, Giulliana; Trevelin, Leonardo; Tavares, Valeria; Alves, Ronnie (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e Computacional, 2023)
Detecting and classifying bat species in mining region caves is crucial for ore extraction activities, environmental impact reduction, and worker safety. This study focuses on using bat echolocation call data collected by VALE, a mining company, to develop a deep learning-based system for species recognition. By applying transfer learning on a pre-trained MobileNetV2 model, the system achieved an impressive 95.21% accuracy in classifying spectrograms of bat echolocation calls from three different species. This outperformed other models tested. Implementing this system would enhance VALE's cave inspection processes, ensuring worker safety and bat population preservation in mining regions.