ECO AI’S AUTOMATED ANIMAL IDENTIFICATION AND DETECTION PROJECT

Artificial Intelligence

Detecting and classifying animal species serves as a foundational step in assessing their long-term viability and the impact our actions may have on them. Additionally, this process aids in the recognition of both predatory and non-predatory animals, both of which pose substantial threats to both humans and the environment. Moreover, it contributes to the reduction of traffic accidents in various regions, where animal encounters on roadways have led to numerous automobile collisions. Nonetheless, the task of detecting and classifying animal species is fraught with challenges, including variations in size and disparate behaviors among species. This paper presents an innovative approach, employing a novel two-stage network with a modified multi-scale attention mechanism, to create an integrated system that effectively addresses these challenges. At the regional proposal stage, we adopt a pyramid design with lateral connections, enhancing the sensitivity of semantic characteristics for smaller objects. Furthermore, we employ a densely connected convolutional network to enhance functional transmission and multiplex it throughout the classification stage, resulting in more precise classification with fewer parameters. Our project demonstrates that deep neural networks, a cutting-edge form of artificial intelligence, can autonomously extract such data. The ultimate goal is to train neural networks for automatic animal identification and recognition, a step forward in harnessing the potential of these technologies.

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