摘要
针对水下海洋生物检测任务存在的目标间相互遮挡、对细长型目标检测精度低、小目标众多等问题,提出一种基于YOLOv5的水下目标检测算法。该算法通过引入可变形卷积、空洞卷积和注意力机制来重新设计主干网络,增强特征提取能力,解决目标间相互遮挡和对细长型目标检测精度低的问题;同时,提出加权显示视觉中心特征金字塔模块,解决特征融合不充分问题,降低小目标漏检率;并调整YOLOv5算法的网络结构,增加融合注意力机制的小目标检测层,提升对小目标物体的检测能力。实验结果表明:改进后的YOLOv5算法在URPC数据集上的平均精度均值达87.8%,较原始YOLOv5算法提高了5.3百分点;同时检测速度保持在34 frame/s;在水下目标检测任务中能够有效提高精确度,降低漏检率和错检率。
In this study,a YOLOv5-based underwater object detection algorithm is proposed to address the challenges of mutual occlusion among underwater marine organisms,low detection accuracy for elongated objects,and presence of numerous small objects in underwater marine biological detection tasks.To redesign the backbone network and improve feature extraction capabilities,the algorithm introduced deformable convolutions,dilated convolutions,and attention mechanisms,mitigating the issues of mutual occlusion and low detection accuracy for elongated objects.Furthermore,a weighted explicit visual center feature pyramid module is proposed to address insufficient feature fusion and reduce the number of failed detections for small objects.Moreover,the network structure of YOLOv5 is adjusted to incorporate a small object detection layer that uses the fused attention mechanism,improving the detection performance for small objects.Experimental results reveal that the improved YOLOv5 algorithm achieves a mean average precision of 87.8% on the URPC dataset,demonstrating a 5.3 percentage points improvement over the original YOLOv5 algorithm while retaining a detection speed of 34 frame/s.The proposed algorithm effectively improves precision and reduces missed and false detection rates in underwater object detection tasks.
作者
陶洋
钟邦乾
赵文博
周昆
Tao Yang;Zhong Bangqian;Zhao Wenbo;Zhou Kun(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2024年第12期431-440,共10页
Laser & Optoelectronics Progress
基金
国家重点研发计划(2019YFB2102001)。
