基于改进YOLO v5s算法的大豆叶片虫洞的识别

Soybean leaf wormhole recognition based on improved YOLO v5s algorithm

鉴于对大豆叶片虫洞进行识别有助于及时发现虫情并有针对性的防治虫害,提出了一种大豆叶片虫洞的识别方法:以YOLO v5s网络作为基础,在大豆叶片虫洞特征提取过程中引入空洞卷积代替3次池化处理,提取虫洞边缘不规则信息;将特征信息输入空间注意力机制,提取时空融合信息,进而捕获野外不同背景下的颜色信息;针对大豆叶片虫洞目标远近不一的问题,重构特征金字塔结构,增加了1层输出层,将80像素×80像素输出特征图经过上采样后得到160像素×160像素特征图,并将其与浅层同尺寸特征图进行拼接,提高虫洞目标识别定位的准确性;将融合后的总特征输入目标检测模块,输出单个对象的检测外框,得到大豆叶片虫洞识别模型。在大豆叶片虫洞样本数据集上对模型进行测试,结果对大豆叶片虫洞的平均识别准确率最高达95.24%,模型存储空间为15.1 MB,每秒传输91帧。所建立的方法与Faster R–CNN、YOLO v3、YOLO v5s对比,对大豆叶片虫洞识别的平均准确率分别提高2.50%、12.13%、2.81%。

Soybean(Glycine max) leaf wormholes seriously affect the quality of crops. However, due to the complex background environment, dense planting and diversified leaf wormhole shapes, traditional manual and machine learning recognition are difficult to meet the requirements in terms of accuracy and speed. In response to this problem, this paper proposes an improved soybean pest identification method. This method is based on the YOLOv5s(You Only Look Once)network, introduces an attention mechanism to improve the recognition ability of wormhole parts, uses the sample transformation method to adapt to the diversity of multi-leaf morphology, and improves the redundant bounding box The elimination mechanism reduces misjudgments and missed judgments. In the experiment, this paper constructed a soybean sample data set as the test data, and compared this method with the traditional deep target recognition method. The average accuracy rate on the test data set is up to 95.24%, and the model storage space is 15.1 MB, the number of frames transmitted per second is 91 f/s. The average accuracy rate is 2.50%, 12.13%, 2.81% higher than Faster R-CNN, YOLO v3,and YOLO v5s respectively. The method proposed in this paper has greatly improved the recognition accuracy and recognition speed, and only requires a small model deployment. The above features make this method more suitable for the practical application of soybean wormhole recognition.