基于YOLOv4神经网络的小龙虾质量检测方法

设计了一种采用YOLOv4深度学习算法的小龙虾质量检测模型,该算法在网络架构、数据处理、特征提取等方面进行了优化。自主拍摄小龙虾图片并进行数据扩充,使用LableImage平台进行数据标注,在Darknet框架下进行网络模型训练,通过对比,模型最终性能均高于其他常见目标检测模型,其检测准确率达97.8%,平均检测时间为37 ms,表明该方法能够有效检测生产过程中的小龙虾质量。

Crayfish quality detection method based on YOLOv4

A crayfish quality detection model using YOLOv4 deep learning algorithm is designed.The algorithm is optimized in terms of network architecture,data processing,and feature extraction.The crayfish image data is collected by video capture and image expansion,and then the data is annotated by LableImage platform.The model is trained under the Darknet framework.By contrast,the final model performance is higher than other common target detection models,and the detection accuracy rate is 97.8%,the average detection time is 37 ms,which proves that the method can effectively detect the quality of crayfish in the production process.