CECS-CLIP: Fusing Domain Knowledge for Rare Wildlife Detection Model.

Simple Summary: Accurate detection of wildlife, particularly small and hidden animals, is crucial for conservation efforts. Traditional image-based methods often struggle in complex environments. This study introduces a novel approach that combines image and text data to improve detection accuracy. By incorporating textual information about animal characteristics and leveraging a Concept Enhancement Module (CEM), our model can better understand and locate animals, even in challenging conditions. Experimental results demonstrate a significant improvement in detection accuracy, achieving an average precision of 95.8% on a challenging wildlife dataset. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This represents a substantial improvement compared to existing state-of-the-art methods. Our multimodal approach offers a promising solution for enhancing wildlife monitoring and conservation efforts. Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. [ABSTRACT FROM AUTHOR]