To address challenges in early forest fire detection—including complex environmental backgrounds, indistinct texture features of small flame/smoke targets, and high computational demands in resource-constrained deployments—we propose YOLO-VRG, a lightweight detection algorithm based on improved YOLOv5s. First, we employ VanillaNet as the feature extraction backbone to significantly reduce model complexity while maintaining efficient feature capture. Second, we design the RVBC3EMA module with spatial-channel reconstruction attention to minimize feature redundancy and enhance discriminative representation. Third, we implement grouped shuffle convolution to further optimize parameter efficiency. Experimental results demonstrate that YOLO-VRG achieves 87.6% mAP@0.5 (3.2% improvement over baseline) with only 2.1 M parameters (74.1% reduction) and 4.5 GFLOPs (71.9% reduction). This balanced architecture enables superior detection accuracy and hardware efficiency for edge deployment scenarios.