基于深度学习的核桃外壳缺陷检测

实现了对核桃外壳缺陷的快速识别，提高基于机器视觉的核桃分选效率，提出了一种基于改进的YOLOv5s核桃外壳缺陷检测方法.YOLOv5s网络中大量采用卷积核为3的卷积进行特征提取，为降低网络的计算量，提出利用深度可分离卷积代替残差网络中所采用的卷积核为3的卷积，提高对核桃外壳检测的速度.此外，为了保证精度能够满足要求，采用了改进的均值聚类对检测框进行初始化，提高生成检测框的质量，进而提高核桃外壳缺陷检测精度.由于聚类方法相对整个网络结构计算量较小，因此对核桃外壳检测的速度影响较小.通过实验对比分析，改进后的YOLOv5s能够快速识别出核桃外壳缺陷，而且识别精度基本保持不变.

Walnut Shell Defect Detection based on Deep Learning

In order to realize the rapid recognition of walnut shell defects and improve the walnut sorting efficiency based on machine vision, a walnut shell defect detection method based on improved yolov5s is proposed. In yolov5s network, convolution with convolution kernel size 3 is widely used for feature extraction. In order to reduce the amount of network calculation, this paper proposes to use depth wise separable convolution instead of convolution with convolution kernel size 3 in residual network, so as to improve the speed of walnut shell detection. In addition, in order to ensure that the accuracy can meet the requirements, this paper also uses the improved mean clustering to initialize the detection frame, improve the quality of the generated detection frame, and then improve the detection accuracy of walnut shell defects. Because the clustering method has less computation than the whole network structure, it has little impact on the speed of peach shell detection. Through experimental comparison and analysis, the improved yolov5s can quickly identify walnut shell defects, and the recognition accuracy remains basically unchanged.