Abstract: In recent years, climate change and ocean pollution have led to the degradation of coral reefs, making automatic coral detection an urgent need for monitoring marine ecosystems. The low image contrast, complex coral shapes, and dense growth in underwater coral detection tasks limit the performance of general detection algorithms. To address these problems, a soft coral detection model based on the YOLO architecture, named CoralDet, has been proposed. Firstly, a multi-path fusion module (MPFB) is designed to capture coral features at multiple scales, which improves the robustness of the model against uneven underwater lighting and image blurring. Additionally, reparameterization is used to enhance inference efficiency. Secondly, introducing GSConv and VoV GSCSP lightweight design components can reduce computational costs without sacrificing performance. An Adaptive Power Transformation label assignment strategy was introduced to dynamically adjust anchor point matching metrics, and soft labels and soft center region loss were used to focus the model on high-quality, aligned and accurate predictions. Finally, CoralDet was evaluated on the Soft Coral dataset with an inference delay of only 9.52 milliseconds and an mAP50 of 81.9, surpassing YOLOv5 (79.9), YOLOv6 (79.4), YOLOv8 (79.5), YOLOv9 (78.3), YOLOv10 (79.5), MambaYOLO (80.1), and RT-DETR (81.6). Experiments were conducted on the Coral-lwptl dataset, and CoralDet outperformed traditional models such as MambaYOLO, YOLOv8, and YOLOv10 in multiple key indicators. The results have demonstrated the effectiveness and practicality of CoralDet in underwater coral detection.

摘要： 近年来，气候变化和海洋污染导致珊瑚礁退化，珊瑚自动检测成为海洋生态系统监测的迫切需求，水下珊瑚检测任务中图像对比度低、珊瑚形状复杂和生长密集等问题限制了通用检测算法的性能。针对上述问题，提出了一种基于YOLO架构的软珊瑚检测模型CoralDet，首先，设计多路径融合模块 (MPFB) 来捕捉多个尺度的珊瑚特征，针对水下不均匀光照和图像模糊现象提高了模型的鲁棒性，同时使用重新参数化来提高推理效率。其次，引入GSConv和VoV-GSCSP轻量级设计组件，可在不牺牲性能的情况下降低计算成本。引入了一种自适应幂变换（APT)标签分配策略来动态调整锚点匹配度量，并且使用了软标签和软中心区域损失以使模型专注于高质量、对齐准确的预测。最后，在Soft-Coral 数据集上对CoralDet进行评估，推理延迟仅为9.52 毫秒，平均精度均值（mAP）50 达到 81.9，超过了 YOLOv5（79.9）、YOLOv6（79.4）、YOLOv8（79.5）、YOLOv9（78.3）、YOLOv10（79.5）、MambaYOLO（80.1）和RT-DETR（81.6），并在 Coral-lwptl 数据集上进行了泛化实验，CoralDet在多个关键指标上均优于 MambaYOLO、YOLOv8 和 YOLOv10 等传统模型，结果证明了 CoralDet 在水下珊瑚检测方面的有效性和实用性。