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## Deep segregation of plastic (DSP): segregation of plastic and nonplastic using deep learning

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Due to industrialization and urbanization, the rapid rise in the volume and amount of hazardous waste and the disposal of it is becoming a burgeoning problem that the world is facing today. One of the best ways out for this problem is to collect, sort and reuse or recycle these waste. This work proposes christened deep segregation of plastic (DSP) architecture which sorts waste materials into plastic and nonplastic using deep learning technique, convolutional neural network (CNN). CNN is one among the efficient modern machine learning techniques, which is able to provide maximum learning efficiency by taking raw input samples. CNN has become a stateof-the-art method for many of the tasks existing in computer vision (CV). In most of the tasks, it has performed well in comparison to the human. This has performed well in various tasks of CV compared to standard neural network. The developed framework is highly scalable on commodity hardware server. The framework collects data from different sensors, preprocess, and analyze using distributed algorithms. Now this framework is specifically developed for plastic segregation. Moreover, the framework can be easily extended to handle large volumes of other waste categories by adding additional resources. These characteristics have made the proposed framework stand out from any other system of similar kind. The proposed design also consists of a prototype which acts as a real-time classifier. The hardware setup consists of a conveyor belt over which the waste materials are placed, and these are captured by a camera fitted on the system. The captured image is sent to the DSP which classifies it into plastic and nonplastic, and accordingly it is moved to two different bins. This system can reduce the human efforts in separating plastics from nonplastics and also in keeping the environment neat and clean. The performance of the system is analyzed on various data sets. These data sets are collected from public and private sources. Various experiments are run for identifying the optimal parameters for CNN networks and structures. All these experiments are run till 1,000 epoch with varied learning rate 0.01-0.5.

Chapter Contents:

• 8.1 Introduction
• 8.2 Related work
• 8.3 Deep learning
• 8.4 Scalable architecture
• 8.5 Software framework
• 8.6 Software and packages
• 8.6.1 TensorFlow
• 8.6.2 Keras
• 8.6.3 OpenCV
• 8.7 Hardware components used
• 8.7.1 Arduino UNO
• 8.7.2 Windshield wiper motor
• 8.7.3 Stepper motor
• 8.7.4 Switching power supply
• 8.7.5 ULN 2003
• 8.7.6 Webcam
• 8.8 Hardware setup for segregation
• 8.9 Experiments and observation
• 8.9.1 Training process
• 8.10 Conclusion and future work
• Appendix A
• Appendix B
• Acknowledgments
• References

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