| 基于改进YOLOV3的自然环境下绿色柑橘的识别算法* |
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| 引用本文： | 宋中山,刘越,郑禄,帖军,汪进.基于改进YOLOV3的自然环境下绿色柑橘的识别算法*[J].中国农机化学报,2021,42(11):159. |
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| 作者姓名： | 宋中山 刘越 郑禄 帖军 汪进 |
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| 作者单位： | 1.中南民族大学计算机科学学院,武汉市,430074; 2.湖北省制造企业智能管理工程技术研究中心,武汉市,430074 |
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| 基金项目： | *湖北省技术创新专项重大项目(2019ABA101);中国科学院—国家民委农业信息技术研究与开发联合实验室招标课题(PJW060012003);中央高校基本科研业务费专项资金项目(CZT19012) |
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| 摘 要： | 为研究自然环境下柑橘的图像识别技术,实现柑橘的早期产量预测,提出一种改进的D-YOLOV3算法,实现自然环境下未成熟的绿色柑橘的识别与检测。研究构建绿色柑橘图像数据集,并对采集的图像进行预处理;改进算法采用DenseNet的密集连接机制替换YOLOV3网络中的特征提取网络Darknet53中的后三个下采样层,加强特征的传播,实现特征的复用。通过自制的数据集对D-YOLOV3算法进行测试,并分别对改进前后网络的识别性能、不同预处理方法和不同数据量图像对模型的影响进行试验。试验结果表明,改进的D-YOLOV3算法相对于传统YOLOV3算法精确率提高6.57%,召回率提高2.75%,F1分数提高4.41%,交并比提高6.13%,平均单张检测时间为0.28 s。通过不同果实数量图像对比试验验证了算法的可行性和准确性。研究结果表明,本文提出的D-YOLOV3算法对自然环境下未成熟的绿色柑橘识别具有较高的精准度,为柑橘的早期测产提供了技术支持。
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| 关 键 词： | 目标检测 YOLOV3算法 DenseNet算法 绿色柑橘 |
| 收稿时间： | 2020-12-08 |
Identification of green citrus based on improved YOLOV3 in natural environment |
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| Abstract: | In order to study the image recognition technology of citrus in natural environment and realize the early yield prediction of citrus, an improved D-YOLOV3 algorithm was proposed. In this study, a green citrus image data set was constructed. In order to enhance the diversity of the data set, preprocessing operations were carried out on the collected images, including color balance, brightness transformation, rotation transformation, blur, and noise. To solve the problem that gradient information in deep networks will disappear or over-expand with the deepening of the network, the improved model adopts DenseNet's dense connection mechanism to replace the last three lower sampling layers of feature extraction network Darknet53 in the YOLOV3 network to enhance the propagation of features and realize feature reuse. The D-YOLOV3 model was tested by the self-made data set, and experiments were conducted on the recognition performance of the network before and after the modification, different pretreatment methods, different amounts of fruit, and different amounts of data images on the model. The experimental results show that compared with the traditional YOLOV3 model, the accuracy rate of the improved D-YOLOV3 model is increased by 6.57%, the recall rate is increased by 2.75%, the F1 score is increased by 4.41%, the intersection ratio is increased by 6.13%, and the average single test time is 0.28 s. Different preprocessing methods enhance the robustness of the model, among which the fuzzy processing has the greatest influence on the performance of the model and the rotation change has the least influence. In the multi-fruit scene, the improved model has a higher recognition accuracy of 5.53% than before, which proves that the model has advantages in recognizing multi-target fruit in the actual scene. Only 1,250 images are needed to fit the model. The research results show that the D-YOLOV3 model proposed in this paper has high accuracy in recognizing immature green citrus in the natural environment, providing technical support for the early production measurement of citrus. |
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| Keywords: | object detection YOLOV3 algorithm DenseNet algorithm green citrus |
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