Chrs-Net: A Dual-Stream YOLO Network for Underwater RGB–Sonar Object Detection

Underwater RGB–sonar object detection remains challenging due to severe optical degradation, strong sonar noise, and spatial misalignment between heterogeneous modalities. Existing multimodal detectors usually rely on simple feature aggregation or limited structural coupling, which cannot effectively model global cross-modal dependencies or address modality-specific degradation. To address these challenges, we propose Chrs-Net, a YOLOv12-based dual-stream framework for underwater RGB–sonar object detection. The proposed network integrates three key components: a Transformer-based Cross-Modal Communication Fusion module (C-mcf) for global cross-modal interaction and semantic alignment, a Multi-Layer Feature Enhancement module (MLFE) for degraded optical feature enhancement, and a Pinwheel-Shaped Convolution module (PConv) for sonar-side structural feature extraction. In addition, an RGB–sonar object detection dataset is constructed for experimental evaluation by relabeling part of the RGBS benchmark, combining simulator-collected samples, and introducing style-transfer-based augmentation to improve data diversity. Experiments on the constructed dataset yield 94.91% mAP@0.5 and 61.10% mAP@0.5:0.95 on the RGB branch, and 94.00% and 57.13% on the sonar branch, respectively, with an inference speed of 53.6 FPS. Compared with representative single-modality and multimodal detectors, Chrs-Net consistently yields superior detection accuracy and localization performance. These results demonstrate that the combination of global cross-modal communication and modality-specific enhancement is effective for robust underwater RGB–sonar object detection in complex environments.