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| 改进YOLOv4的温室环境下草莓生育期识别方法 | |
| 引用本文： | 龙洁花,郭文忠,林森,文朝武,张宇,赵春江. 改进YOLOv4的温室环境下草莓生育期识别方法[J]. 智慧农业(中英文), 2021, 3(4): 99-110. DOI: 10.12133/j.smartag.2021.3.4.202109-SA006 |
| 作者姓名： | 龙洁花 郭文忠 林森 文朝武 张宇 赵春江 |
| 作者单位： | 北京市农林科学院智能装备技术研究中心，北京 100097 上海海洋大学 信息学院，上海 201306 |
| 摘 要： | 针对目前设施农业数字化栽培调控技术中对作物的生育期实时检测与分类问题,提出一种改进YO-LOv4的温室环境下草莓生育期识别方法.该方法将注意力机制引入到YOLOv4主干网络的跨阶段局部残差模块(Cross Stage Partial Residual,CSPRes)中,融合草莓不同生长时期的目标特征信息,同时降低复杂背... |
| 关 键 词： | 目标检测 草莓 生育期识别 YOLOv4 残差模块 注意力机制 损失函数 |
| 收稿时间： | 2021-09-14 |
Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4 |
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| LONG Jiehua,GUO Wenzhong,LIN Sen,WEN Chaowu,ZHANG Yu,ZHAO Chunjiang. Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4[J]. Smart Agriculture, 2021, 3(4): 99-110. DOI: 10.12133/j.smartag.2021.3.4.202109-SA006 | |
| Authors: | LONG Jiehua GUO Wenzhong LIN Sen WEN Chaowu ZHANG Yu ZHAO Chunjiang |
| Affiliation: | Beijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center, Beijing 100097, China College of Information Science, Shanghai Ocean University, Shanghai 201306, China |
| Abstract: | Aiming at the real-time detection and classification of the growth period of crops in the current digital cultivation and regulation technology of facility agriculture, an improved YOLOv4 method for identifying the growth period of strawberries in a greenhouse environment was proposed. The attention mechanism into the Cross Stage Partial Residual (CSPRes) module of the YOLOv4 backbone network was introduced, and the target feature information of different growth periods of strawberries while reducing the interference of complex backgrounds was integrated, the detection accuracy while ensured real-time detection efficiency was improved. Took the smart facility strawberry in Yunnan province as the test object, the results showed that the detection accuracy (AP) of the YOLOv4-CBAM model during flowering, fruit expansion, green and mature period were 92.38%, 82.45%, 68.01% and 92.31%, respectively, the mean average precision (mAP) was 83.78%, the mean inetersection over union (mIoU) was 77.88%, and the detection time for a single image was 26.13 ms. Compared with the YOLOv4-SC model, mAP and mIoU were increased by 1.62% and 2.73%, respectively. Compared with the YOLOv4-SE model, mAP and mIOU increased by 4.81% and 3.46%, respectively. Compared with the YOLOv4 model, mAP and mIOU increased by 8.69% and 5.53%, respectively. As the attention mechanism was added to the improved YOLOv4 model, the amount of parameters increased, but the detection time of improved YOLOv4 models only slightly increased. At the same time, the number of fruit expansion period recognized by YOLOv4 was less than that of YOLOv4-CBAM, YOLOv4-SC and YOLOv4-SE, because the color characteristics of fruit expansion period were similar to those of leaf background, which made YOLOv4 recognition susceptible to leaf background interference, and added attention mechanism could reduce background information interference. YOLOv4-CBAM had higher confidence and number of identifications in identifying strawberry growth stages than YOLOv4-SC, YOLOv4-SE and YOLOv4 models, indicated that YOLOv4-CBAM model can extract more comprehensive and rich features and focus more on identifying targets, thereby improved detection accuracy. YOLOv4-CBAM model can meet the demand for real-time detection of strawberry growth period status. |
| Keywords: | object detection strawberry growth period recognition YOLOv4 residual module attention mechanism loss function |
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