Abstract
In this paper, the technique of image recognition algorithm is used to conduct an in-depth study and analysis of the intelligent classification and recycling system of solid waste and to optimize the design of its system. The network structure and detection principle of the YOLO target detection algorithm based on convolutional neural nets are analysed, images of construction solid waste are collected as a dataset, and the image dataset is expanded using data enhancement techniques, and the target objects in the dataset are labelled and used to train their own YOLO detection models. To facilitate testing the images and to design a YOLO algorithm-based construction solid waste target detection system. Using the detection system for construction solid waste recognition, the YOLO model can accurately detect the location, class, and confidential information of the target object in the image. Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. In this paper, the combination of lazy optimizer and lookahead can improve the generalization ability and fitting speed as well as greatly improve the accuracy and stability. The experimental results are tested, and it is found that the solid waste classification accuracy can be as high as 95% when the VGG19 model is selected and the optimizer is combined.
Highlights
The rapid economic development, people gradually from the solution of subsistence to achieve a well-off life, but the introduction of many industries brought a variety of problems, the most serious of which is the problem of solid waste disposal, a large amount of solid waste cannot be disposed of, causing unprecedented pressure on the environment
50,000 images are selected as the dataset, the convolutional neural network is continuously trained through the training set, the weights and bias values are updated, and the model parameters are preserved by whether the current model loss decreases or not, and after the iteration, the accuracy of the model is tested through the test set and compared with the accuracy of the training set and validation, and the model generalization ability can be seen by the accuracy
The results show that the algorithm can localize a single bottle target, but the different shapes of the bottle target have an impact on the localization accuracy, in which the highest detection accuracy is 90.1% when the can is most complete and 88.7% when the can is more complete, and the detection accuracy of the can in the other two shapes is reduced
Summary
The rapid economic development, people gradually from the solution of subsistence to achieve a well-off life, but the introduction of many industries brought a variety of problems, the most serious of which is the problem of solid waste disposal, a large amount of solid waste cannot be disposed of, causing unprecedented pressure on the environment. Due to the scarcity of materials in the past, people would use the same item many times, and not much solid waste was generated in daily life. If this problem is not solved in time, it will restrict social development and bring immeasurable damage to people’s life. In today’s rapid development of the Internet of Things, machine vision technology is used in various fields such as education, medical, and military [1,2,3]. The technology has been widely used since its inception, especially in recent years, the development is rapid, common in life such as face recognition, expression recognition, license plate recognition, and other
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