Design of fruit classification algorithm based on improved convolutional neural network
Fruit classification is of great significance for fruit production,processing,transportation,and self-service sales.Convolutional neural networks efficiently process and classify vast amounts of fruits through multi-level feature learning and automatic feature extraction,showcasing outstanding advantages in fruit classification.However,current fruit classification methods face numerous issues such as excessive reliance on manual labor,suboptimal accuracy,insufficient intelligence,and poor robustness.To address these challenges,an improved YOLOv3 CNN algorithm for fruit classification was proposed.By utilizing the LabelImg tool for data annotation and replacing the YOLOv3 backbone network,draknet53,with the DenseNet network,dense connections between network layers were established.This enhancement reinforced the feature information of fruit images,enabled feature reuse,reduced computational parameters,strengthened feature training,and consequently,resulted in a highly accurate fruit classification model.Tests demonstrated that the improved algorithm achieved an average accuracy rate of 98%in fruit classification recognition,significantly enhancing the precision of fruit sorting.
fruit classificationconvolutional neural networkDenseNet modelYOLOv3data annotationsfeature reuse
万方数据
维普
