| 基于改进YOLOv4模型的全景图像苹果识别 |
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| 引用本文： | 周桂红,马帅,梁芳芳.基于改进YOLOv4模型的全景图像苹果识别[J].农业工程学报,2022,38(21):159-168. |
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| 作者姓名： | 周桂红 马帅 梁芳芳 |
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| 作者单位： | 河北农业大学信息科学与技术学院，保定 071001；河北省农业大数据重点实验室，保定 071001 |
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| 基金项目： | 国家自然科学基金（62106065） |
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| 摘 要： | 苹果果园由于密植栽培模式，果树之间相互遮挡，导致苹果果实识别效果差，并且普通的图像采集方式存在图像中果实重复采集的问题，使得果实计数不准确。针对此类问题，该研究采用全景拍摄的方式采集苹果果树图像，并提出了一种基于改进YOLOv4和基于阈值的边界框匹配合并算法的全景图像苹果识别方法。首先在YOLOv4主干特征提取网络的Resblock模块中加入scSE注意力机制，将PANet模块中的部分卷积替换为深度可分离卷积，且增加深度可分离卷积的输出通道数，以增强特征提取能力，降低模型参数量与计算量。将全景图像分割为子图像，采用改进的YOLOv4模型进行识别，通过对比Faster R-CNN、CenterNet、YOLOv4系列算法和YOLOv5系列算法等不同网络模型对全景图像的苹果识别效果，改进后的YOLOv4网络模型精确率达到96.19%，召回率达到了95.47%，平均精度AP值达到97.27%，比原YOLOv4模型分别提高了1.07、2.59、2.02个百分点。采用基于阈值的边界框匹配合并算法，将识别后子图像的边界框进行匹配与合并，实现全景图像的识别，合并后的结果其精确率达到96.17%，召回率达到95.63%，F1分数达到0.96，平均精度AP值达到95.06%，高于直接对全景图像苹果进行识别的各评价指标。该方法对自然条件下全景图像的苹果识别具有较好的识别效果。
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| 关 键 词： | 图像识别 YOLOv4 苹果 scSE 深度可分离卷积 边界框匹配合并 |
| 收稿时间： | 2022/5/31 0:00:00 |
| 修稿时间： | 2022/10/24 0:00:00 |
Recognition of the apple in panoramic images based on improved YOLOv4 model |
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| Zhou Guihong,Ma Shuai,Liang Fangfang.Recognition of the apple in panoramic images based on improved YOLOv4 model[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(21):159-168. |
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| Authors: | Zhou Guihong Ma Shuai Liang Fangfang |
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| Institution: | College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China; Hebei Key Laboratory of Agricultural Big Data, Baoding 071001, China |
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| Abstract: | Yield forecasting is one of great significance for decision-making in the apple industry, including labor hiring, harvesting, and storage allocation. Traditional forecasting of apple yield relies mainly on the manual counting of some of the apple trees to estimate the yield of the entire apple orchard. The inaccurate prediction cannot fully meet the large-scale production in recent years. Therefore, it is a high demand for a more accurate and labor-saving way to forecast apple orchard yield. Artificial intelligence in smart orchards can be expected to combine with traditional orchards in the development of the apple industry. The accurate recognition of apples is one of the key technologies to achieve the intelligent yield estimation of apple orchards. However, the shading between apple trees has posed a great challenge to apple fruit identification at present, due to the dense cultivation mode in apple orchards. The repeated capture of apple fruit images can lead to inaccurate fruit counting in the image-collecting mode in each fruit tree. In this study, a panoramic image of apple recognition was proposed using an improved YOLOv4 and threshold-based bounding box matching and merging algorithm. Panoramic photography was used to collect the images of apple fruit trees. Firstly, the Spatial-Channel Sequeeze & Excitation (scSE) attention modules were added to the Resblock module of the backbone of YOLOv4. Some convolutions in the PANet module and YOLO Head module were replaced by the depthwise separable convolutions. The number of output feature channels of depthwise separable convolutions increased to enhance the feature extraction capability, but to reduce the number of model parameters and computation. The panoramic image was segmented into several sub-images. The improved YOLOv4 model was selected to recognize the apples in the sub-images. A comparison was performed on the recognized data of different network models, such as the Faster R-CNN, CenterNet, YOLOv4, YOLOv4-Lite, YOLOv5-l, and YOLOv5-x for the panoramic images of apple trees. The improved YOLOv4 network model achieved a precision rate of 96.19%, a recall rate of 95.47%, and an AP value of 97.27, which were 1.07, 2.59, and 2.02 percentage points higher than the original YOLOv4 model. Secondly, the bounding boxes of the apples in the recognized sub-images were matched and merged by the threshold-based bounding box matching and merging, in order to realize the recognition of panoramic images. The validation experiments determined that the thresholds of 3, 1, and 45 were used for the D1, D2, and D3, respectively. A better performance was achieved in the precision rate of 96.17%, a recall rate of 95.63%, an F1 score of 0.96, and an AP value of 95.06%, which were higher than each evaluation index for the direct recognition of panoramic images of apples. As such, the panoramic image recognition was obtained to merge the sub-image recognition using the improved YOLOv4 model. The higher evaluation index and better recognition were achieved in the apple recognition of panoramic images under natural conditions. The finding can provide a new strategy to recognize apple fruits for the intelligent measurement of orchard yield. |
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| Keywords: | image recognition YOLOv4 apple scSE depthwise separable convolution matching and merging of bounding box |
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