结合YOLOv3算法和Blob检测技术计算尼罗罗非鱼种子

Diana Tri Susetianingtias, Eka Patriya, Rini Arianty
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引用次数: 0

摘要

幼鱼计数必须准确计数,以免造成任何损失,特别是小鱼种子或鱼种的体积较小。一般来说,人们仍然使用产生低精度值的传统计数方法。本研究将利用YOLOv3算法和Blobb检测技术编制Nila幼鱼鱼种计数器程序。注释数据处理将使用LabelImg,数据集训练将在在线环境中使用带有Darknet框架的Google协作实验室。将在此程序中预测的图像将被调用并使用对象检测器检测。置信分数大于0.3的对象将被转换为blob。blob值将被转发到输出层,用于缩放边界框对象。该程序的输出是预测图像,blob值,预测时间和Nila种子的数量。使用混淆矩阵对模型性能进行评价,准确率达到98.87%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combination of YOLOv3 Algorithm and Blob Detection Technique in Calculating Nile Tilapia Seeds
Baby Fish counting must be counted accurately so it will not cause any loss, especially for fish seeds or fingerlings that have a small size. Generally, people still use conventional counting methods that produce low accuracy values. This research will make a Nila Baby Fish fingerlings counter program using the YOLOv3 algorithm and Blobb detection technique. The annotation data process will use LabelImg, and the dataset training will use Google COLABoratory with the Darknet framework in an online environment. Images that will predict in this program will be called and detected with an object detector. The object with a confidence score of more than 0.3 will be converted into a blob. The blob value will be forwarded to the output layer for scaling the bounding box objects. The output of this program is the predicted image, blob value, prediction time, and the number of Nila seeds. The model performance is evaluated using a confusion matrix and got a 98.87% for accuracy score.
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