ECO SCAN - AI - POWRED ANIMAL RECOGNITION AND SPECIES CATEGORIZATION

Information Technology

It is essential to identify and categories animal species in order to evaluate their long-term survival and the potential effects of our actions on them. This procedure also helps identify predatory and non-predatory species, both of which represent serious risks to both people and the environment. Additionally, it helps to lessen traffic accidents in several areas where contacts with animals on the road have caused countless car accidents. However, obstacles like size differences and divergent behaviour between species make it difficult to identify and categories animal species. In order to build an integrated system that successfully addresses these issues, the novel two-stage network and modified multi-scale attention mechanism presented in this research are used. We adopt a pyramid design with lateral connections at the regional proposal stage to increase the sensitivity of semantic properties for smaller objects. In order to improve functional transmission and multiplex it across the classification step, we also use a densely linked convolutional network, which leads to more accurate classification with fewer parameters. Our experiment showcases the autonomous data extraction capabilities of deep neural networks, a cutting-edge type of artificial intelligence. To fully utilize the potential of these technologies, the ultimate goal is to train neural networks for autonomous animal identification and recognition.

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