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

Area of Science:

• Ecology
• Computer Science
• Artificial Intelligence

Background:

• Traditional wildlife monitoring relies on image-based methods, which are limited in detecting small, occluded, or camouflaged animals in complex environments.
• Accurate and efficient wildlife monitoring is crucial for effective conservation strategies.

Purpose of the Study:

• To develop an innovative multimodal target detection framework integrating textual information from an animal knowledge base with image features.
• To enhance the performance of wildlife detection, particularly for challenging targets like small or camouflaged animals.

Main Methods:

• A concept enhancement module using cross-attention mechanisms to fuse textual and image features, creating enhanced image representations.
• A feature normalization module to amplify cosine similarity and introduce learnable parameters for feature transformation, improving feature expressiveness.
• Validation using a specialized dataset from Northwest A&F University.

Main Results:

• The multimodal model achieved a 0.3% improvement in precision compared to single-modal methods.
• Demonstrated at least a 25% improvement in Average Precision (AP) over existing multimodal algorithms.
• Showed superior performance in detecting small targets of specific species, surpassing current benchmarks.

Conclusions:

• The proposed multimodal target detection model effectively integrates textual and image data for improved wildlife monitoring.
• This approach offers significant advancements for the conservation of rare and endangered wildlife.
• Provides new perspectives and strong evidence for multimodal detection research in conservation biology.