YOLO-LSD: A LIGHTWEIGHT MODEL FOR HIGH-ACCURACY MULTI-BREED SHEEP FACE DETECTION

Sheep face detection is critical for intelligent livestock management and breeding, yet existing models often struggle in complex farm scenarios due to inadequate multi-scale feature utilization and high computational demands. To address these challenges, this study proposes a lightweight multi-breed sheep face detection framework named YOLO-LSD (Lightweight Sheep Face Detection), achieving an optimal balance between detection accuracy and computational efficiency through multi-dimensional optimizations. At the feature enhancement level, the lightweight channel attention mechanism Efficient Channel Attention (ECA) is embedded into the backbone network to dynamically strengthen the channel responses of key facial features through local cross-channel interactions. Concurrently, Ghost convolution is introduced to replace traditional convolutional layers, leveraging feature redundancy mining technology to substantially reduce computational complexity while maintaining the ability to represent diverse facial features across sheep and goat breeds. To address the limited sample diversity in multi-breed datasets, a transfer learning strategy is employed, involving directional fine-tuning of breed-specific facial features based on large-scale pre-trained models to enhance the model's generalization ability across diverse sheep and goat varieties. Experimental results demonstrate that YOLO-LSD achieves a [email protected] of 99.29% on a self-constructed multi-breed sheep face dataset, marking a 0.59% improvement over the baseline YOLOv11. Notably, the parameter count of YOLO-LSD is only 2.4×106, while achieving an inference speed of 60 FPS and 6.3 Flops. This study presents a high-precision, lightweight solution for intelligent livestock monitoring systems, offering practical insights for the deployment of multi-breed sheep face detection models in real-world farm applications.