改进的YOLOv5蛋类缺陷自动检测模型

目的:解决现有蛋类缺陷图像自动检测方法存在的检测效率低、精度差等问题。方法:在蛋类检测系统的基础上,提出一种改进的YOLOv5自动检测模型。将轻量级网络MobileNetv3添加到YOLOv5模型中,以降低模型复杂度,删除颈部网络和输出端小目标检测。结果:与传统的控制方法相比,该方法能够更准确、高效地实现蛋类目标表面缺陷检测,复杂度降低了35%以上,单幅图像的检测时间为14.25 ms,检测准确率＞95%,满足食品缺陷检测的需要。结论:改进的YOLOv5检测模型可以有效提高蛋类缺陷检测效率。

An improved automatic detection model for egg defection based on YOLOv5

Objective: To solve the problems of low detection efficiency and poor accuracy of existing automatic detection methods for egg defect images. Methods: Based on the egg detection system, an improved YOLOv5 automatic detection model was proposed. Added the lightweight network MobileNetv3 to YOLOv5 model to reduce the complexity of the model, and deleted the neck network and small target detection at the output end. Results: Compared with the traditional control method, this method can detect the surface defects of egg targets more accurately and efficiently, with the complexity of more than 35% reducing, the detection time of a single image of 14.25 ms, and the detection accuracy rate over 95%, which meet the needs of food defect detection. Conclusion: The improved YOLOv5 detection model can effectively improve the detection efficiency of egg defects.