湖北农业科学2025，Vol.64Issue(4)：7-13,30,8.DOI:10.14088/j.cnki.issn0439-8114.2025.04.002
基于改进Faster R-CNN模型的丁岙杨梅成熟度检测方法
Improved Faster R-CNN model-based maturity detection method for Ding'ao bayberry
摘要
Abstract
To rapidly and accurately detect the maturity levels of Ding'ao bayberry(Myrica rubra)in complex natural growth environ-ments,an improved Faster R-CNN model(ConvNeXt-T+SE+FPN)-based maturity detection method was proposed.ConvNeXt-T was adopted as the backbone feature extraction network to enhance detection capabilities in complex scenarios.The SE attention mecha-nism and Feature Pyramid Network(FPN)were introduced to improve the model's sensitivity to maturity-related features and detec-tion of small-target fruits.Compared to ResNet50,ConvNeXt-T+SE,ConvNeXt-T+FPN,and ConvNeXt-T+SE+FPN increased the mean average precision(mAP)by 14.75%,19.85%,and 21.86%,respectively.The ConvNeXt-T+SE+FPN configuration achieved the largest mAP improvement,effectively enhancing detection performance for different maturity levels of Ding'ao bayberry.Through training and testing on the Ding'ao bayberry image dataset,the improved Faster R-CNN model demonstrated high accuracy in detect-ing different maturity levels.The average precision(AP)for unripe,semi-ripe,near-ripe,and fully ripe fruit recognition was 96.90%,94.63%,95.91%,and 97.58%,respectively,with an mAP of 96.26%.Compared to the original Faster R-CNN model,the improved model achieved a 21.86%increase in mAP.The improved Faster R-CNN model effectively enhanced the detection accuracy of Ding'ao bayberry maturity,providing strong support for intelligent harvesting of bayberry fruits.关键词
丁岙杨梅(Myrica rubra)/改进Faster R-CNN模型/成熟度/检测Key words
Ding'ao bayberry(Myrica rubra)/improved Faster R-CNN model/maturity/detection分类
信息技术与安全科学引用本文复制引用
刘玉耀,彭琼尹..基于改进Faster R-CNN模型的丁岙杨梅成熟度检测方法[J].湖北农业科学,2025,64(4):7-13,30,8.基金项目
浙江省教育厅一般科研项目基金资助项目(Y202352993) （Y202352993）
