VM-RTDETR: Advancing DETR with Vision State-Space Duality and Multi-Scale Fusion for Robust Pig Detection

Pig detection is a fundamental yet challenging task in intelligent livestock farming, primarily due to difficulties in capturing both global contextual information and multi-scale features within complex environments. To address this, we propose VM-RTDETR, a novel detection model based on an enhanced RT-DETR architecture. The model incorporates a Vision State-Space Duality (VSSD) backbone, leveraging a novel Non-Causal State-Space Duality (NC-SSD) mechanism to overcome the limitations of traditional SSMs by enabling efficient modeling of long-range dependencies and global context. Furthermore, we design a Multi-Scale Efficient Hybrid Encoder (M-Encoder) that employs parallel convolutional kernels to capture both local details and global contours, effectively addressing scale variations. The synergistic design of the VSSD backbone and the M-Encoder enables our model to achieve more comprehensive feature representation. Experimental results on a custom dataset of 8070 images from a pig farm (with 6955 images for training and 1115 for testing) demonstrate that VM-RTDETR significantly outperforms existing mainstream detectors, improving AP, AP50, and AP75 by up to 2.35%, 0.63%, and 2.76%, respectively, over the strong R50-RTDETR baseline. Our model significantly enhances detection robustness in complex scenarios, offering an efficient and accurate solution for intelligent livestock farming.