重庆工商大学学报（自然科学版）2026，Vol.43Issue(3)：61-69,9.DOI:10.16055/j.issn.1672-058X.2026.0003.008
基于改进的YOLOv7河道漂浮物小目标检测模型
A Small Target Detection Model for River Floating Objects Based on Improved YOLOv7
摘要
Abstract
Objective In the operation of cleaning river floating objects using robots,limited computational capacity and complex river environments often lead to the missed detection of small targets.To address these issues,an improved YOLOv7 object detection model that balances accuracy and lightweight design is proposed based on YOLOv7.Methods First,a small target detection layer with a size of 160×160 was added to the original YOLOv7 to enhance the feature learning ability for small objects.The BiFormer attention mechanism was introduced into the neck of the original network model to improve detection performance for small targets,thereby reducing missed detections during river floating object detection.Second,the ELAN module in the backbone was replaced with PELAN to reduce the number of parameters and computational overhead,thus increasing the detection speed.On this basis,the MPDIoU loss function was incorporated to accelerate the convergence and improve the accuracy of bounding box regression and enhance the robustness of the network.Results The improved model achieved a mean average precision(mAP)of 71.5％on the FloW subset from Oka-Zhibo,which was 4.7％higher than that of the original YOLOv7 model.Moreover,the number of parameters and computational cost were reduced by 11％and 7.3％,respectively.Conclusion Comparative experiments show that the overall performance of the improved model is better than that of the original network model and traditional classic target-detection network models.The proposed model can reduce the model complexity while improving the accuracy,making it more suitable for practical applications.关键词
小目标检测/YOLOv7网络模型/BiFormer/轻量化/损失函数Key words
small target detection/YOLOv7 network model/BiFormer/lightweight/loss function分类
资源环境引用本文复制引用
王国明,宋健..基于改进的YOLOv7河道漂浮物小目标检测模型[J].重庆工商大学学报（自然科学版）,2026,43(3):61-69,9.基金项目
国家自然科学(青年)基金(62102003)项目资助 （青年）
安徽省大学生创新创业基金(S202310361157,S202310361161)项目资助. （S202310361157,S202310361161）
