YOLIP: An Enhanced Framework for UAV-Assisted Wildlife Monitoring Based on YOLO Integrated with the CLIP Model.

UAV-based wildlife monitoring encounters tremendous challenges posed by complex environments, such as the extremely low proportion of effective targets in aerial images and variations in remote sensing scales. This paper presents a novel fusion framework named YOLIP, which integrates a detection head with semantic perception capabilities and an implicit feature adjustment module to boost detection accuracy and feature representation ability. Specifically, this paper redesigns the detection head to enable it to simultaneously learn spatial positioning and semantic features, thereby achieving more reliable extraction of regional features. The implicit feature modulation module introduces a dual-path fusion mechanism, which elevates the feature quality through geometric-semantic fusion, thereby improving the consistency and robustness of the detection. Furthermore, this paper also develops an asynchronous scheduling strategy, which can selectively execute computationally intensive operations to achieve computational optimization, enabling this framework to adapt to actual detection scenarios based on unmanned aerial vehicles. In this study, we conducted numerous experiments on the self-built drone wildlife dataset as well as the publicly available aerial wildlife dataset. Theresults demonstrate that compared with existing detection models, YOLIP improves mAP@0.5 by 11.6% while maintaining an efficient inference speed, achieving an improvement in detection performance. In addition, cross-dataset evaluation verifies the stable performance and generalization capability of the proposed method across multiple real-world scenarios.