| 林探宇1，肖德琴1，2，曾基业1，殷建军1，黄顺斌1.基于区域生长和SVM 结合的黄金大蚊快速检测算法[J].广东农业科学,2016,43(7):172-177 |
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基于区域生长和SVM 结合的黄金大蚊快速检测算法 |
| Fast detection algorithm for golden crane fly based on region growing and SVM |
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| DOI：10.16768/j.issn.1004-874X.2016.07.026 |
| 中文关键词: 农业图像处理 支持向量机 区域生长算法 黄金大蚊 |
| 英文关键词: agricultural image processing support vector machine region growing golden crane fly |
| 基金项目:国家星火计划项目（2013GA780002，2015GA780002）；国家级大学生创新项目（201410564287） |
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| 摘要点击次数: 1591 |
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| 中文摘要: |
| 结合传统图像处理方法与支持向量机（Support Vector Machine，SVM）技术，设计了一种黄金大蚊图像快速检测算法，对现场采集的黄金大蚊图像进行快速检测。该方法实现了在分割过程中完成对害虫种类的标记，简化了图像处理步骤。同时，利用SVM 支持小样本数据，解决了训练中样本数量的问题。从现实环境中拍摄得到100 张黄金大蚊图片为素材进行分类器的训练与检测，得到识别率较高的分类器，利用所得到的分类器结合传统图像处理方法设计与实现了本检测算法。通过对80 幅现场试验照片检测分析显示，对黄金大蚊的正确识别率可达到92% 以上。对于目标较为明显的图片，算法运行时间在0.2 s 以内。算法达到较快运行速度和较高精度，对田间害虫快速监测提供了技术支撑，具有较好的应用前景。 |
| 英文摘要: |
| A fast detection algorithm for golden crane fly based on the traditional image processing methods
and Support Vector Machine was designed to detect the golden crane fly in real environment faster. In this
scheme，pest was realized in the process of segmentation which simplified the image processing. Besides，the SVM
method not only supported small sample data training of classifier，but also it reduced the number of samples in
the training. We took 100 pictures of golden crane fly in the natural environment as the materials for the classifier
training and got high recognition rate of the classifier. Meanwhile，we also realized the algorithm using the
classifier combined with the traditional image processing methods. By detecting 80 pictures of golden crane fly，
it showed that the recognition correct rate for golden crane fly reached more than 90%. Besides，for a clear target
in our picture，the detection time of algorithm was less than 0.2 second. For the faster running speed and higher
precision of the algorithm，it could provide the technical support for fast monitoring of pests on the field and had
good application prospect. |
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