An Image Oriented AI Methodology to Detect Animal Footprints Based on Enhanced Neural Optimization and Classification Scheme

The identification and detection of animal footprints are essential for the conservation of wildlife, ecological studies, and wildlife monitoring. This paper introduces a novel method for detecting animal footprints that combines deep learning and image processing. The approach, known as the Enhanced Neural Optimization and Classification Scheme (ENOCS), is cross-validated with the conventional model, the Support Vector Machine (SVM), to assess the efficacy of the proposed scheme. Our approach employs a learning model that has been fined-tuned on a dataset of various types of animal print to extract and classify features with high accuracy. The quality of the input data is enhanced through the image-processing component by eliminating noise, normalizing illumination, and performing morphological operations to draw emphasis to footprint patterns. Performance metrics are tested on a benchmark dataset containing a wide range of animal traces and evaluated with conventional image recognition methods. The results indicate that our method attains significant improvement in accuracy relative to the state-of-the-art technique, known as SVM. Furthermore, our approach is invariant to variations in environmental conditions and image quality. The proposed scheme thus provides a reliable and effective tool to identify animal imprints, thereby increasing the effectiveness of measures for ecosystems and conservation.