| 基于改进Faster R-CNN模型的樱桃缺陷检测 |
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| 引用本文： | 魏冉,裴悦琨,姜艳超,周品志,张永飞.基于改进Faster R-CNN模型的樱桃缺陷检测[J].食品与机械,2021,37(10):98-105. |
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| 作者姓名： | 魏冉 裴悦琨 姜艳超 周品志 张永飞 |
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| 作者单位： | 大连大学辽宁省北斗高精度位置服务技术工程实验室,辽宁 大连 116622；大连大学大连市环境感知与智能控制重点实验室,辽宁 大连 116622 |
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| 基金项目： | 国家自然科学基金项目（编号：61601076）；大连市高层次人才创新计划（编号：2019RQ070） |
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| 摘 要： | 目的：提高工业环境下樱桃分级分拣工作的效率。方法：提出了基于Faster R-CNN框架改进的樱桃缺陷识别分拣模型。结果：通过对比VGG16、MobileNet-V2和ResNet50网络，主干网络为ResNet50的效果最优，改进后的Faster R-CNN模型对樱桃裂口、双生、刺激生长、霉变、褐变腐烂和完好果的检测精度分别为97.75%，99.77%，98.90%，97.56%，96.67%，98.80%，平均检测精度达98.24%，高于其他模型，检测速度为31.16帧/s。结论：试验方法对樱桃缺陷类别的检测具有较高的识别精度。
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| 关 键 词： | 樱桃 缺陷 Faster R-CNN 特征金字塔 注意力机制 |
| 收稿时间： | 2021/4/16 0:00:00 |
Detection of cherry defects based on improved Faster R-CNN model |
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| WEIRan,PEIYuekun,JIANGYanchao,ZHOUPinzhi,ZHANGYongfei.Detection of cherry defects based on improved Faster R-CNN model[J].Food and Machinery,2021,37(10):98-105. |
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| Authors: | WEIRan PEIYuekun JIANGYanchao ZHOUPinzhi ZHANGYongfei |
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| Affiliation: | Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian, Liaoning 116622, China; Dalian University Environment Sensing and Intelligent Control Key Laboratory of Dalian, Dalian University, Dalian, Liaoning 116622, China |
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| Abstract: | Objective: To improve the efficiency of cherry classification and sorting in industrial environment. Methods: An improved cherry defect recognition and sorting model based on Faster R-CNN framework was proposed. Results: By comparing VGG16, MobileNet-V2 and ResNet50 network, the effect of Resnet50 network was the best, the improved Faster R-CNN model had 97.75%, 99.77%, 98.90%, 97.56%, 96.67%, 98.80% of detection precision for cherry fissure, twinning, growth stimulation, mildew, Browning rotten and intact fruit, respectively. The average detection accuracy of the improved Faster R-CNN model was 98.24%, which was higher than other models, and the detection speed was 31.16 frames/s. Conclusion: The test method had a high identification accuracy for cherry defects. |
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| Keywords: | cherry defects Faster R-CNN feature pyramid attentional mechanism |
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