Deep vision-based surveillance system to prevent train–elephant collisions

Animal conservation is imperative, and technology can certainly assist in different ways. The extinction of endangered species like tigers and elephants has boosted the necessity for such efforts. Human–elephant collision (HEC) has been an active area of research for years. Apart from deforestation, the roads and rail tracks laid down through forest areas intervene a lot in wildlife. Collisions and tragedies are every day, especially in green belts in India and other Asian countries. Therefore, it is crucial to develop vision-based, automated, warning-generating systems to identify the animal/elephant near-site. In the proposed work, different deep learning-based models are proposed to identify elephants in image/video. Several convolutional neural network (CNN)-based models and three transfer learning (TL)-based models, i.e., ResNet50, MobileNet, Inception V3, have been experimented with and tuned for elephant detection. All the models are tested on a synthesized dataset having about 4200 images built using two public datasets, i.e., ELPephant and RailSem19. Two accurate CNN and transfer learning-based models are presented in detail. These highly accurate and precise models can alarm the trains and generate warning signals on site. The proposed CNN and inception network demonstrated high accuracy of 99.53% and 99.91%, respectively, and are remarkable in identifying elephants and hence preventing HEC. The same model can be trained for other animals for their preservation in similar scenarios.