Hou Jiankang, Zhang Cunyong
School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang, 222005, China.
Heliyon. 2024 May 10;10(10):e31029. doi: 10.1016/j.heliyon.2024.e31029. eCollection 2024 May 30.
Submarine mud poses a risk to channel navigation safety. Traditional detection methods lack efficiency and accuracy. As a result, this paper proposed an enhanced shallow submarine mud detection algorithm, leveraging an improved YOLOv5s model to increase accuracy and effectiveness in identifying such hazards in marine channels. Firstly, the sub-bottom profiler was employed to assess the submarine channel of Lianyungang Port to acquire the image data of the shallow mud sound print. Concurrently, the analysis incorporated the characteristics of changes in sound intensity peaks to precisely identify the shallow mud's location. Furthermore, the incorporation of C2F feature module into the backbone module enhances the gradient flow of the algorithm, augments the feature extraction information, and improves the algorithm's detection performance. Subsequently, Efficient Multi-Scale Attention (EMA) mechanism is incorporated into the neck module, aiming to optimize the algorithm's channel dimensions, minimize computational overhead, and enhance its detection efficiency. Finally, the study introduced Normalized Wasserstein Distance (NWD) loss function into bounding box regression loss function. This integration effectively addresses the issue of multi-scale defects, emphasizes the transformation of target planar position deviation, and improves the accuracy of the algorithm's detection capabilities. The results indicate that the improved YOLOv5s-EF algorithm outperforms the original YOLOv5s algorithm and other widely used detection algorithms. It achieved a validation set precision rate of 97.8%, recall rate of 97.6%, F1 value of 97.7%, mean Average Precision (mAP)@0.5 of 98.2%, mAP@0.95 of 69.6%, and Frames Per Second (FPS) of 51.8. YOLOv5s-EF algorithm proposed in this study offers a novel technical approach for detecting mud in submarine channels, which is importance for ensuring the safe operation and maintenance of dredging in such channels.
海底淤泥对航道航行安全构成威胁。传统检测方法缺乏效率和准确性。因此，本文提出了一种增强型浅海海底淤泥检测算法，利用改进的YOLOv5s模型提高在海洋航道中识别此类危险的准确性和有效性。首先，采用浅地层剖面仪对连云港港的海底航道进行评估，获取浅淤泥声纹的图像数据。同时，该分析结合声强峰值变化特征来精确识别浅淤泥的位置。此外，将C2F特征模块融入主干模块，增强了算法的梯度流，增加了特征提取信息，提高了算法的检测性能。随后，将高效多尺度注意力（EMA）机制融入颈部模块，旨在优化算法的通道维度，最小化计算开销，并提高其检测效率。最后，该研究将归一化瓦瑟斯坦距离（NWD）损失函数引入边界框回归损失函数。这种整合有效解决了多尺度缺陷问题，强调了目标平面位置偏差的变换，提高了算法检测能力的准确性。结果表明，改进后的YOLOv5s-EF算法优于原始的YOLOv5s算法和其他广泛使用的检测算法。它在验证集上的准确率为97.8%，召回率为97.6%，F1值为97.7%，平均精度均值（mAP）@0.5为98.2%，mAP@0.95为69.6%，每秒帧数（FPS）为51.8。本研究提出的YOLOv5s-EF算法为海底航道淤泥检测提供了一种新颖的技术方法，对确保此类航道疏浚的安全运行和维护具有重要意义。
