Deep-learning-based garbage identification evolutionary learning method, device, system and medium

Abstract

The invention discloses a garbage identification evolutionary learning method, a device, a system and a medium based on depth learning. The method comprsies steps of acquiring garbage sample image data; Preprocessing the garbage sample image data; The pre-processed garbage sample image data is used as the input parameter of the neural network, and is compared with the trained garbage recognition model. According to the comparison result, whether the recognition is successful or not is judged. Feeding back the corresponding information of the identified garbage to the garbage sorting and placing mechanism; The image data of garbage sample which failed to be recognized is recognized again by ResNet algorithm, the image data of garbage sample which is successfully recognized by ResNet algorithm is marked, and the corresponding garbage information is fed back to the garbage sorting and putting mechanism, and the garbage recognition model is updated. The garbage sample image data of garbagesample which failed to be recognized is identified again by ResNet algorithm. The successful garbage sample images identified by ResNet algorithm are transmitted to users or maintainers for marking and updating the garbage identification model. The invention greatly reduces the workload of the maintenance personnel and realizes the accurate classification of a large amount of garbage.

Description

technical field [0001] The invention relates to a garbage identification method, in particular to a garbage identification evolutionary learning method, device, system and storage medium based on deep learning, belonging to the technical field of garbage identification. Background technique [0002] The origin of machine learning can be traced back a long time ago, and deep learning is also within the category of machine learning. As a new field in the field of machine learning, it appeared in 2006 and its development momentum has not diminished in recent years. The structure of deep learning is compared with the traditional shallow learning institutions. Compared with the shallow structure, the structure of deep learning is more complicated, and the number of training layers of the neural network involved is more, so it is more complex than the traditional shallow structure. The learning structure can learn more abstract features of the data, and it is more sensitive to the...

AI Technical Summary

Problems solved by technology

[0003] The first object of the present invention is to provide a garbage recognition evolutionary learning method based on deep learning, which improves the current garbage recognition technology, and at the same time uses the data with poor recognition effect as the evolutionary power to update and improve the trained garbage recognition model in real time, Make the trained garbage recognition model have the real-time and accuracy that other garbage collection devices do not have, and greatly reduce the workload of maintenance personnel, realize the accurate classification of a large amount of garbage, and solve the problem that the current garbage recognition device recognizes the limited amount of garbage and the lack of accuracy At the same time, in the process of work, continuous self-updating and optimization

Images