The proliferation of clean energy infrastructure has accelerated the global deployment of wind farms, necessitating enhanced mitigation strategies for bird conservation. This study presents a computationally efficient detection algorithm designed to minimize the ecological impact of wind turbines on bird populations. Our primary contributions include three technical innovations: (1) A Dilated Channel Shuffle Squeeze-and-Excitation (DCSSE) attention module that achieves parameter efficiency while preserving detection accuracy; (2) An Adaptive Scale Efficient Intersection over Union (AS-EIoU) loss function optimized for small-target detection; and (3) Integration of PP-LCNet’s lightweight architecture for feature extraction. The proposed FB-YOLO framework demonstrates superior performance compared to the YOLOv8 benchmark across all evaluation metrics. Quantitative analysis reveals significant improvements: precision (+13.57%), recall (+18.86%), mean Average Precision (mAP)50 (+15.48%), and mAP50-95 (+7.89%), accompanied by a model weight reduction of 3.3 MB. Comparative evaluations with alternative detection models confirm FB-YOLO’s dual advantage in achieving higher accuracy with reduced computational complexity. To support this research, we introduce an annotated bird dataset comprising 6,038 high-resolution images. Experimental results on this dataset demonstrate FB-YOLO’s robust performance (95.6% precision, 94.9% recall, 96.4% mAP50, and 43.4% mAP50-95). The optimized architecture demonstrates significant potential for practical implementation in wind farm monitoring systems, offering an effective technological solution for bird collision prevention through real-time detection capabilities.