YOLO-MSLT: A Multimodal Fusion Network Based on Spatial Linear Transformer for Cattle and Sheep Detection in Challenging Environments

Accurate detection of cattle and sheep is a core task in precision livestock farming. However, the complexity of agricultural settings, where visible light images perform poorly under low-light or occluded conditions and infrared images are limited in resolution, poses significant challenges for current smart monitoring systems. To tackle these challenges, this study aims to develop a robust multimodal fusion detection network for the accurate and reliable detection of cattle and sheep in complex scenes. To achieve this, we propose YOLO-MSLT, a multimodal fusion detection network based on YOLOv10, which leverages the complementary nature of visible light and infrared data. The core of YOLO-MSLT incorporates a Cross Flatten Fusion Transformer (CFFT), composed of the Linear Cross-modal Spatial Transformer (LCST) and Deep-wise Enhancement (DWE), designed to enhance modality collaboration by performing complementary fusion at the feature level. Furthermore, a Content-Guided Attention Feature Pyramid Network (CGA-FPN) is integrated into the neck to improve the representation of multi-scale object features. Validation was conducted on a cattle and sheep dataset built from 5056 pairs of multimodal images (visible light and infrared) collected in the Manas River Basin, Xinjiang. Results demonstrate that YOLO-MSLT performs robustly in complex terrain, low-light, and occlusion scenarios, achieving an [email protected] of 91.8% and a precision of 93.2%, significantly outperforming mainstream detection models. This research provides an impactful and practical solution for cattle and sheep detection in challenging agricultural environments.