A Hybrid CNN-BiLSTM Approach for Wildlife Detection Nearby Railway Track in a Forest

Wildlife conflict has become a serious concern due to increasing animal mortality from rail-induced accidents on railway tracks passing through the forest region. Monitoring the movement of wild animals near a railway track remains challenging due to the complex terrain, varied landscapes, and diverse biodiversity. This paper presents an optimized hybrid one-dimensional Convolutional Neural Network – Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture to classify wildlife and other ground activities from seismic data generated in a forest environment. The proposed method automatically searches the high-level patterns sequentially from the multi-domain features that are extracted from the principal modes of Variational Mode Decomposition (VMD) of seismic signals. Further, the classification results are compared with the standalone Convolutional Neural Network (CNN) and Bidirectional-Long Short-Term Memory (BiLSTM), where the proposed method outperforms with an average accuracy of 78.11 ± 4.28% and the lowest false detection rate.