Efficient YOLOv12 for Multi-Scale Object Detection

Abstract This paper presents five optimized variants of YOLOv12, designed to improve computational efficiency while maintaining detection accuracy. The modifications involve pruning and reconfiguring the YOLOv12 model’s structure to enhance resource utilization. Each proposed model is tailored to specific object sizes: YOLOv12-Small (small objects), YOLOv12-Medium (medium objects), YOLOv12-Large (large objects), YOLOv12-SM (small and medium objects), and YOLOv12-ML (medium and large objects). To select the proper proposed model based on dataset characteristics, we developed a simple program that identifies the dataset’s object size distribution}. Experimental evaluations with the baseline YOLOv12 model demonstrate that the proposed models achieve superior computational efficiency while preserving detection accuracy. In addition, the proposed models have been compared with YOLOv8, YOLOv10, and YOLOv11, demonstrating improvements in model size and GFLOPs while achieving comparable accuracy. However, its inference time and power consumption are moderately greater than those of the earlier YOLO versions.