A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning

Abstract

Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model.

A fault diagnosis method for flexible converter valve equipment based on DSC-BIGRU-MA

“…In recent years, deep learning methods have gained significant popularity in the field of fault diagnosis due to their ability to extract fault features at a deeper level and achieve high diagnostic accuracy (Yuan and Liu, 2022). For instance, in the case of insulator strings on transmission lines, researchers (Zhou et al, 2022) presented deep convolutional generative adversarial network to capture comprehensive fault data characteristics, which enables accurate diagnosis of insulator string faults and defects, even under conditions of strong background noise.…”