Image recognition model building in the context of municipal waste classification

Abstract

To address the challenge of inadequate classification rate of the collected waste, the garbage classification image recognition model is designed based on machine vision technology, and after comparing the test effect of a variety of common network models after the collected 12 types of municipal household waste image dataset, it is found that Inception-ResNetV2 and Xception have significantly better recognition effects on the training and validation sets, with Inception-ResNetV2 performing better overall. This model was utilized as the pre-training model, and the model's output side was repeatedly tweaked for different parameters without affecting its internal stem and Inception-Resnet-A/B/C modules until the ideal model and related optimized parameters were identified. A confusion matrix was created to assess the accuracy and completeness of each form of garbage, and the model was shown to be better suited for identifying the majority of practical waste categorization applications such as biological, battery, cardboard, paper, and shoes.