Enhancing Sun-Dried Kelp Detection: Introducing K-YOLO, a Lightweight Model with Improved Precision and Recall
文献类型： 外文期刊
作者： Xiao, Zhefei 1 ; Zhu, Ye 1 ; Hong, Yang 1 ; Ma, Tiantian 1 ; Jiang, Tao 1 ;
作者机构： 1.Chinese Acad Fishery Sci, Fishery Machinery & Instrument Res Inst, Shanghai 200092, Peoples R China
关键词： deep learning; kelp; drying; attention mechanism; YOLOv5; optical images; UAV
期刊名称：SENSORS （ 影响因子：3.9； 五年影响因子：4.1 ）
ISSN：
年卷期： 2024 年 24 卷 6 期
页码：
收录情况： SCI
摘要： Kelp, often referred to as a "sea vegetable", holds substantial economic significance. Currently, the drying process for kelp in China primarily relies on outdoor sun-drying methods. Detecting kelp in the field presents challenges arising from issues such as overlapping and obstruction. To address these challenges, this study introduces a lightweight model, K-YOLOv5, specifically designed for the precise detection of sun-dried kelp. YOLOv5-n serves as the base model, with several enhancements implemented in this study: the addition of a detection head incorporating an upsampling layer and a convolution module to improve the recognition of small objects; the integration of an enhanced I-CBAM attention mechanism, focusing on key features to enhance the detection accuracy; the replacement of the CBS module in the neck network with GSConv to reduce the computational burden and accelerate the inference speed; and the optimization of the IoU algorithm to improve the identification of overlapping kelp. Utilizing drone-captured images of sun-dried kelp, a dataset comprising 2190 images is curated. Validation on this self-constructed dataset indicates that the improved K-YOLOv5 model significantly enhances the detection accuracy, achieving 88% precision and 78.4% recall. These values represent 6.8% and 8.6% improvements over the original model, respectively, meeting the requirements for the real-time recognition of sun-dried kelp.
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