Improved YOLOv5-Based Inland River Floating Garbage Detection Model
基于改进YOLOv5的内河漂浮垃圾检测模型作者机构：College of Artificial IntelligenceTianjin University of Science and TechnologyTianjin 300457China
出 版 物：《印刷与数字媒体技术研究》 (Printing and Digital Media Technology Study)
年 卷 期：2024年第5期
页 面：195-204页
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Floatinggarbage YOLOv5 Attentionmechanism Multi-scale detection head Focal-EIoU
摘 要：Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water *** address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this *** order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone ***,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and *** Focal-EIoU loss function was also employed to optimize the model parameters,improving localization *** results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.
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