Efficient feature selection and fusion for real-time beef cattle detection

Real-time and accurate beef cattle detection is essential for effective livestock management. Traditional manual observation methods are labor-intensive and inefficient. Recent studies have shown that deep learning has significantly improved beef cattle detection accuracy. However, achieving robust beef cattle detection remains challenging due to single farming scenarios, occlusions, and dense cattle groups. As an effective solution, this paper proposes a novel method for efficient feature selection and fusion for real-time beef cattle detection (EFSF-RBCD). Specifically, we begin by developing a feature extraction network based on multipath cooperative and poly kernel inception (MPCPKI), which is designed to optimize the feature extraction capabilities. The network includes an efficient P4 feature-layer selection module based on the multipath cooperative gating mechanism (EP4MCGM), which integrates low-level features from shallow layers and enhances fine detail detection. Additionally, the P5 feature layer selection module, based on the cross-stage partial poly kernel inception network (CSPPKINetP5), enables efficient target feature extraction while reducing the computational load. Furthermore, we propose a frequency-domain context feature fusion network (FDCFN), a novel framework that integrates the frequency-domain branch (FDB) and context feature fusion branch (CFFB) to capture local and global contextual information better. Additionally, to enhance detection accuracy, a novel bounding box regression loss function, SIoU, was introduced, which improves bounding box position and size estimation by incorporating orientation information between the ground truth and predicted boxes. Experimental results show that EFSF-RBCD achieves an [email protected] of 90.3% and an [email protected]–0.95 of 59.6%, with 26.4M parameters, a computational cost of 50.8 GFLOPs, and a processing speed of 100.3 FPS. The proposed method outperforms existing state-of-the-art methods in terms of [email protected] and [email protected]–0.95 while maintaining a low parameter count and computational load. Additionally, it demonstrated competitive performance in terms of FPS. This study provides a new approach for beef cattle detection in complex environments and lays a theoretical foundation for the development of technologies related to smart-farm deployment.