Research on Image Recognition of Pandas and Bears Based on Deep Learning

Abstract

As deep learning technology has rapidly developed in recent years, image recognition and classification have become a hotspot research direction in artificial intelligence. Accurate identification of target objects in images has great significance for practical applications like autonomous driving, medical image analysis, and biodiversity conservation. Pandas and brown bears are both endangered species of worldwide conservation concern, but manually classifying large volumes of images can be challenging, especially for applications requiring high speed and precision. This study aims to leverage the powerful capabilities of convolutional neural networks (CNNs) to automatically and accurately distinguish between pandas and brown bears. CNNs can learn discriminative visual features from large labeled image datasets and have demonstrated state-of-the-art performance on various image recognition benchmarks. Applying these deep learning techniques has the potential to provide a new technological solution for the panda-brown bear identification challenge, which is crucial for effective wildlife monitoring and conservation strategies. The proposed approach involves training a CNN model using a comprehensive dataset of panda and brown bear images. The model's classification accuracy and inference speed are thoroughly evaluated. The research findings are expected to contribute to enhanced scale and efficiency of field data collection for protecting these vulnerable species.