Attention-Enhanced ResNet (AEResNet) is an advanced deep learning framework that integrates the robust Residual Network (ResNet) with Convolutional Block Attention Modules (CBAM). Designed to accurately detect penguins and turtles, AEResNet addresses the complexities of natural habitats by enhancing feature sensitivity and precision with attention mechanisms. Employing a carefully curated dataset that includes images of both species, the AEResNet model outperforms conventional ResNet models, demonstrating superior Intersection over Union (IoU) and Average Precision (AP) metrics. The effectiveness of AEResNet is validated through extensive training and validation phases, showcasing its robustness and generalization capabilities. The findings underscore the potential of attention-enhanced architectures in wildlife monitoring, significant implications for biodiversity conservation by enabling precise and minimally invasive ecological data collection. This study lays the groundwork for future investigations into the application of similar architectures for broader ecological monitoring and species detection.