农业机械学报2025，Vol.56Issue(5)：425-432,8.DOI:10.6041/j.issn.1000-1298.2025.05.040
基于改进MBS-YOLO v8的火龙果目标检测与定位方法
Pitaya Fruit Target Detection and Localization Method Based on Improved MBS-YOLO v8
摘要
Abstract
Aiming to address the issue of overlapping occlusion caused by the varying sizes and large quantities of dragon fruit,a multi-scale weighted feature fusion network(MBS-YOLO v8)was proposed based on the YOLO v8 model.Firstly,the squeeze-and-excitation attention(SEAttention)mechanism was incorporated into the feature extraction module to enhance the network's ability to capture critical details,thereby addressing the challenge of small object detection.Secondly,a multi-scale weighted fusion network(MWConv)was introduced to generate feature maps with varying receptive fields,improving the capture of global features within images.Finally,experimental results demonstrated that MBS-YOLO v8 achieved an accuracy of 92.5％,a recall rate of 90.1％,and a mean average precision(mAP50)of 94.7％.Compared with the YOLO v8n algorithm,MBS-YOLO v8 showed improvements of 2.1 percentage points,5.9 percentage points,and 2 percentage points in accuracy,recall,and mAP50,respectively.The proposed MBS-YOLO v8 model exhibited high robustness,effectively integrating global feature information with low-dimensional local features to enhance the model's understanding of image content.This approach effectively addressed challenges related to overlapping occlusion and small object detection,providing an improved methodology for detecting dragon fruit and other similar targets.关键词
火龙果/目标检测/小目标/全局特征/多尺度加权特征融合网络Key words
pitaya fruits/object detection/small object/global features/multi-scale weighted feature fusion network分类
信息技术与安全科学引用本文复制引用
刘进一,晏伏山,董赫,付丽荣,付威,陈雨..基于改进MBS-YOLO v8的火龙果目标检测与定位方法[J].农业机械学报,2025,56(5):425-432,8.基金项目
海南省科技人才创新项目(KJRC2023D38)和海南大学协同创新中心项目(XTCX2022STC16) （KJRC2023D38）
