Comparative Analysis of Real-Time Detection Models for Intelligent Monitoring of Cattle Condition and Behavior

The present study provides a systematic benchmarking of nine state-of-the-art object detection models adapted to a specialized animal dataset. The main objective was to evaluate their performance in terms of accuracy and inference speed in the context of real-time and commercial applications within the agro-industrial sector. The method-ology involved aggregating and thoroughly cleaning several cattle image datasets, fol-lowed by fine-tuning models with diverse architectures — two-stage, one-stage, and transformer-based. Performance evaluation was conducted on a unified hardware platform equipped with an NVIDIA RTX 4090 GPU with 24 GB of VRAM, which is critical for the objec-tive assessment of speed-related metrics. Key results show that the D-FINE and Co-DETR models demonstrated the highest ac-curacy (AP@[IoU=0.50:0.95] of 0.872 and 0.851, respectively), while RTMDet and YOLOv11 proved to be the fastest (15.81 ms/image and 19.14 ms/image in reproducible mode). A general trend was observed toward significant improvement in average pre-cision (AP) metrics on the specialized dataset compared to their known results on gen-eral-purpose datasets such as COCO, while relative inference speed rankings remained largely consistent.