DyMC-YOLO: Complementary Mask-Guided Fusion and Dynamic Multi-Path Prediction for Multimodal RGB–Infrared Object Detection

This paper proposes DyMC-YOLO, an efficient RGB–infrared object detector based on YOLOv13. The evaluated infrared modalities include thermal infrared in the M3FD benchmark and near-infrared in a self-constructed RGB–NIR grape dataset. To balance cross-modal interaction with computational cost, the model integrates a streamlined dual-stream backbone, Complementary Mask-Guided Feature Fusion (CMFF), and a Dynamic Multi-Path Detection Head (DMP-Detect). After validation-based checkpoint selection, final evaluation on held-out test sets yielded mAP@0.5:0.95 scores of 51.73% on M3FD and 83.06% on the grape dataset, outperforming EarlyFusion-YOLOv13 by 4.31 and 1.75 percentage points, respectively. Operating at 45.87 FPS with 8.026 M parameters and 19.216 GFLOPs on an NVIDIA L40S GPU, the model provides an accurate and computationally efficient solution for multimodal RGB–infrared object detection.