Abstract
According to the waste type identification requirement in waste classification, a waste type identification method based on a bird flock neural network (BFNN) was proposed. The problem of obtaining the feature dataset of waste images was considered, and color histogram and texture feature extraction techniques were used. The local optimum problem of a typical backpropagation neural network (BPNN) was considered, and a bird flock optimization (BFO) algorithm was proposed. The accuracy problem of the typical BPNN was considered, and a new online weight adjustment method of neurons was proposed. The number of hidden layer neurons (nodes) of the typical BPNN was considered, and an online adjustment method was proposed. The experimental results show that the recyclables (paper, plastic, glass, and cloth) and nonrecyclables can effectively be identified by the waste type identification method based on the BFNN, and the recognition accuracy is 81% which meets actual needs.
Highlights
“Shanghai Household Waste Management Regulation” had been implemented since July 2019. e domestic waste was divided into recyclables and nonrecyclables based on the regulation
The study on NN and related optimization algorithm has been a research hotspot. e differential evolution- (DE-) based metaheuristic optimization algorithm was used for optimizing the search space of artificial neural network (ANN), and the effect of ANN-DEbased model predictions was better than the traditional quadratic model predictions [5]
A NN was used for designing sensors, the parameters were modeled by ANN and optimized by genetic algorithm (GA) within the practical levels, and the optimization process led to find the most accurate optimal conditions possible [11]
Summary
“Shanghai Household Waste Management Regulation” had been implemented since July 2019. e domestic waste was divided into recyclables (including paper, plastic, glass, metal, and cloth) and nonrecyclables (including hazardous, wet, and dry waste) based on the regulation. An intelligent waste material classification system was proposed, which was developed by using the 50-layer residual net pretrained convolutional neural network (CNN) model [3]. It is well known that CNN can directly extract image features, which is an important algorithm especially suitable for image classification [12,13,14]. According to the above problems, a bird flock neural network (BFNN) was proposed, and a waste type identification method based on the BFNN was proposed. E image features in Figure 1 were extracted to generate dataset, and the dataset was used as the input data of NN. Identification method based on the BFNN was used to identify some recyclables (paper, plastic, glass, and cloth) and nonrecyclables by image features. Each individual emulates its most successful neighbor by updating its velocity and position to
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