基于改进YOLOv4算法的玻璃杯缺陷识别方法研究

当前，玻璃杯表面缺陷检测主要依赖人力劳动来完成，存在耗时长且准确率不高等问题。提出了一种将YOLOv4与MobileNetV3结合的改进算法模型YOLO-M来解决该问题。首先，利用MobileNetv3网络替换YOLOv4原本的主干网络CSPDarknet53,并修改激活函数，在减少模型大小和参数量的基础上提升运行速度。然后，对玻璃杯缺陷样本进行拍照采样，将缺陷分为磨损、气泡、划痕三种，建立玻璃杯缺陷数据集。最后利用YOLO-M、YOLOv4以及YOLOv4-tiny三种算法对玻璃杯缺陷数据集进行训练，将不同算法下的平均精度均值、帧率等评价指标进行对比。实验结果表明，YOLO-M算法在玻璃杯缺陷检测上的帧率达到57.72 f/s,平均精度均值达到91.95%,均为最高。YOLO-M算法在玻璃杯缺陷识别的速度和精度上有明显效果，可做为后续分拣研究，以及其他玻璃制品缺陷识别的重要参考。

Research on glass defect recognition method based on improved YOLOv4

At present, glass surface defect detection is mainly manual, which takes a long time and has low accuracy. An improved algorithm model YOLO-M combining YOLOv4 and mobilenetv3 is proposed to solve this problem. Firstly, the mobilenetv3 network is used to replace the original backbone network cspdarknet53 of YOLOv4, and the activation function is modified to improve the running speed on the basis of reducing the model size and parameters. Then, the glass defect samples were photographed and sampled. The defects were divided into wear, bubble and scratch, and the glass defect data set was established. Finally, the glass defect data set is trained by using YOLO-M, YOLOv4 and YOLOv4 tiny algorithms, and the evaluation indexes such as average precision and frame rate under different algorithms are compared. The experimental results show that the frame rate of YOLO-M algorithm in glass defect detection is 57.72 f/s, and the average accuracy is 91.95%. YOLO-M algorithm has obvious effect on the speed and accuracy of glass defect recognition, and can be used as an important reference for subsequent sorting research and other glass product defect recognition.