Deep Learning for Plastic Waste Classification System

Abstract

Plastic waste management is a challenge for the whole world. Manual sorting of garbage is a difficult and expensive process, which is why scientists create and study automated sorting methods that increase the efficiency of the recycling process. The plastic waste may be automatically chosen on a transmission belt for waste removal by using methods of image processing and artificial intelligence, especially deep learning, to improve the recycling process. Waste segregation techniques and procedures are applied to major groups of materials such as paper, plastic, metal, and glass. Though, the biggest challenge is separating different materials types in a group, for example, sorting different colours of glass or plastics types. The issue of plastic garbage is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material). Therefore, we should look for ways to separate this waste. One of the opportunities is the use of deep learning and convolutional neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene, polypropylene, and polystyrene. The main problem considered in this article is creating an automatic plastic waste segregation method, which can separate garbage into four mentioned categories, PS, PP, PE-HD, and PET, and could be applicable on a sorting plant or home by citizens. We proposed a technique that can apply in portable devices for waste recognizing which would be helpful in solving urban waste problems.

Recent Advancements in Plastic Packaging Recycling: A Mini-Review

“…Bobulski and coworkers implemented new portable devices for computer image recognition in combination with artificial intelligence for waste recognition and easy municipal waste separation. The devices were used both at home and in waste sorting plants, and they could be a very useful tool for an efficient and economically sustainable separation of plastic waste stream [107].…”

Assessment of Performance and Challenges in Use of Commercial Automated Sorting Technology for Plastic Waste

“…Sorting robots, guided by AI, can either operate as an alternative traditional optical sorters or can supplement optical sorters by purging incorrectly sort plastics at the end of the sorting process [16]. Moreover, AI sorters have the ability to im prove sorting efficiency over time by using available data to mimic a human brain's lear ing and decision-making processes [46][47][48]. A total of seven AI-based sorters are reported here from seven different companie all with the ability to sort plastic by type and color (Table 6).…”

A Survey on Waste Detection and Classification Using Deep Learning

“…According to the results of the evaluation, they conclude that simple CNN networks with or without residual blocks perform well. Besides a single object detection, a single trash class was investigated by [70].…”