计量学报2025，Vol.46Issue(10)：1461-1469,9.DOI:10.3969/j.issn.1000-1158.2025.10.08
YOLOv5-LED水母识别分类算法
Jellyfish Recognition and Classification Algorithm Based on YOLOv5-LED
摘要
Abstract
In response to issues such as low accuracy,poor real-time performance,and limited recognition types in existing jellyfish recognition algorithms,a novel algorithm named YOLOv5-LED for jellyfish recognition and classification is proposed.Firstly,G-Conv module,G-BottleNeck module,and G-C3 module are designed,forming the basis for a Ghost-based feature extraction module.Subsequently,a four-scale feature detection head structure is introduced,along with a feature fusion structure based on bidirectional cross-scale PANet and the incorporation of CBAM attention mechanism,resulting in the creation of a new jellyfish detection and recognition algorithm model YOLOv5-LED.Finally,improvements are made to the IOU loss function by introducing a distribution loss function based on KL divergence to replace the cross-entropy loss function,and the candidate box generation algorithm is enhanced.Moreover,a method based on Cluster NMS is introduced to replace the weighted NMS algorithm in YOLOv5.Experimental results show that with a threshold of 0.5∶0.95,the average detection precision of YOLOv5-LED improved by 2.7%compared to the base YOLOv5.The parameter count decreased by 13.6%,and the computational load reduced by 7.2%.These improvements not only enhance precision but also reduce parameters and computational complexity,achieving network lightweighting.关键词
光学计量/水母检测与识别/YOLOv5-LED/Ghost轻量化/四尺度特征融合Key words
optical metrology/jellyfish detection and recognition/YOLOv5-LED/Ghost lightweight/Four scale feature fusion引用本文复制引用
高美静,尹浩正,傅昊翔,王昆达,燕永浩,解运佳..YOLOv5-LED水母识别分类算法[J].计量学报,2025,46(10):1461-1469,9.基金项目
国家自然科学基金(61971373) （61971373）
河北省自然科学基金(F2023105001) （F2023105001）
