AI SWLM: artificial intelligence-based system for wildlife monitoring - Krishnan, Arun Govindan; Bhuvana, Jayaraman; Thai, Mirnalinee Thanga Nadar Thanga; Ramanujam, Bharathkumar Azhagiya Manavala (International Journal of Electrical and Computer Engineering (IJECE), 2026)
Detection and recognition of wild animals are essential for animal surveillance, behavior monitoring and species counting. Intrusion of animals and the disaster to be caused can be averted by the timely recognition of intruding animals. An artificial intelligence-based system for wildlife monitoring (AI SWLM) is designed and implemented on the camera trap images. The challenges such as detecting and recognizing animals of different sizes, shape, angles and scale, recognizing the animals of same and different species, detecting them under various illumination conditions, with pose variants and occlusion are addressed by identifying the optimal weights of the deep learning architecture, AI SWLM. Models were trained using Gold Standard Snapshot Serengeti dataset with random weights and the best weights of model were used as initial weights for training the augmented data. This has doubled the performance in terms of mean average precision, which can be interpreted.