| 快速精准识别棚内草莓的改进YOLOv4-Tiny模型 |
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| 引用本文： | 孙俊,陈义德,周鑫,沈继锋,武小红.快速精准识别棚内草莓的改进YOLOv4-Tiny模型[J].农业工程学报,2022,38(18):195-203. |
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| 作者姓名： | 孙俊 陈义德 周鑫 沈继锋 武小红 |
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| 作者单位： | 江苏大学电气信息工程学院，镇江 212013 |
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| 摘 要： | 为了实现棚内草莓果实的快速精准识别，该研究提出一种基于改进YOLOv4-Tiny的草莓检测模型。首先，为了大幅度减少模型计算量，采用轻量型网络GhostNet作为特征提取网络，并在GhostBottleneck结构中嵌入卷积注意力模块以加强网络的特征提取能力；其次，在颈部网络中添加空间金字塔池化模块和特征金字塔网络结构，融合多尺度特征提升小目标草莓的检测效果；最后，采用高效交并比损失作为边界框回归损失函数，加速网络收敛并提高模型的检测准确率。结果表明，改进YOLOv4-Tiny模型权重大小仅为4.68 MB，平均每幅图片的检测时间为5.63 ms，在测试集上的平均精度均值达到92.62%，相较于原YOLOv4-Tiny模型提升了5.77个百分点。与主流的目标检测模型SSD、CenterNet、YOLOv3、YOLOv4和YOLOv5s相比，改进YOLOv4-Tiny模型平均精度均值分别高出9.11、4.80、2.26、1.22、1.91个百分点，并且模型权重大小和检测速度方面均具有绝对优势，该研究可为后续果实智能化采摘提供技术支撑。
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| 关 键 词： | 机器视觉 图像处理 果实识别 YOLOv4-Tiny 注意力机制 小目标 |
| 收稿时间： | 2022/9/2 0:00:00 |
| 修稿时间： | 2022/9/2 0:00:00 |
Fast and accurate recognition of the strawberries in greenhouse based on improved YOLOv4-Tiny model |
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| Sun Jun,Chen Yide,Zhou Xin,Shen Jifeng,Wu Xiaohong.Fast and accurate recognition of the strawberries in greenhouse based on improved YOLOv4-Tiny model[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(18):195-203. |
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| Authors: | Sun Jun Chen Yide Zhou Xin Shen Jifeng Wu Xiaohong |
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| Institution: | School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China |
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| Abstract: | Abstract: Detection of fruit images has been one of the most important steps for automatic picking robots. There are many factors that make strawberry detection difficult in an orchard, such as the complex background, fruit occlusion, and small target fruit. In this study, an improved detection model with the YOLOv4-Tiny was proposed to rapidly and accurately recognize the strawberries in the greenhouse for the high detection accuracy of small targets in an orchard. Firstly, the GhostNet lightweight network was adopted to replace the backbone CSPDarkNet53-tiny for the feature extraction, which significantly reduced the parameters and computation of the model. Convolution Block Attention Module (CBAM) with the spatial information was embedded into the Ghost Bottleneck module instead of the original Squeeze-and-Excitation (SE) attention, in order to improve the feature extraction capability. Secondly, the Spatial Pyramid Pooling (SPP) module was introduced in the neck network structure, and then to carry out the maximum pooling operation with three pooling kernels (5×5, 9×9, 13×13). Feature Pyramid Network (FPN) structure was adopted to improve the detection accuracy of small target strawberries. Finally, the Efficient Intersection over Union Loss (EIoU Loss) was used to separate the influence factors of aspect ratio, and then calculate the length and width of the target frame and anchor box. As such, the convergence speed was faster with higher regression accuracy than before. The original data set was collected, consisting of 841 strawberry images with a complex background in the greenhouse. Data enhancement was carried out for the training, the verification, and the test set, in order to improve the generalization ability of the model. The experimental results showed that the average accuracy of the improved YOLOv4-Tiny model in the test set was 92.62%, which was 5.77 percentage points higher than the original one. The average detection time of each image was 5.63ms, and the final model size was only 4.68MB. The average accuracies of the improved model were 9.11, 4.80, 2.26, 1.22, and 1.91 percentage points higher, while the F1 scores were 0.08, 0.05, 0.03, 0.01, and 0.03 higher than those of the SSD, CenterNet, YOLOv3, YOLOv4, and YOLOv5s target detection networks. The improved YOLOv4-Tiny model was much smaller than the SSD, CenterNet, YOLOv3, YOLOv4, where the network structure size was only 1/3 of YOLOv5s. The average detection speed of each image was only 5.63ms, which was reduced by 5.57, 7.14, 9.20, 15.99, and 2.16ms, respectively. Therefore, the improved YOLOv4-Tiny model can fully meet the requirements of high precision and real-time detection of strawberry fruits under the background of the orchard in the greenhouse. The finding can provide an effective way for the accurate detection of strawberry fruits in a complex environment using picking robots. |
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| Keywords: | machine vision image processing fruit identification YOLOv4-Tiny attention mechanism small target |
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