DTNet: A Novel Infrared-Visible Fruit Object Detection Method Based on Dual-Modal Feature Interaction Fusion and Transformer Decoding

Fruit detection in orchard environments is often challenged by illumination variation, leaf occlusion, cluttered backgrounds, and dense distributions of small targets, which limit the robustness of visible-light detectors in practical applications. To address these issues, this study proposes DTNet, an end-to-end RGB-IR dual-modal detection framework for close-range fruit instance recognition. Rather than relying on single-modal appearance cues, DTNet improves detection robustness by jointly exploiting cross-modal complementary perception, fine-grained local detail enhancement, and global relational modeling, thereby strengthening the representation of small, partially occluded, and illumination-sensitive fruit targets. In addition, a multimodal balanced detection loss is introduced to improve optimization stability under class imbalance and hard-sample interference. Experiments on a grape RGB-IR object detection dataset show that DTNet achieves a Precision of 0.9132, a Recall of 0.8931, an mAP@0.5 of 0.9552, and an mAP@0.5:0.95 of 0.8001, outperforming competing methods overall. On an additional tomato RGB-IR dataset, DTNet also maintains stable detection accuracy, indicating favorable adaptability to another fruit RGB-IR detection task. The results indicate that DTNet is a promising approach for robust RGB-IR fruit detection in complex orchard environments.