FYP_review_01_25-11-23 deep learning approach for electronic waste detection , and classification

Final year project review deep learning approach for electronic waste detection , and classification

1. Internal Project Guide: Dr. JIJESH J J HOD and Professor, Dept. of Electronics and communication SVCE, Bengaluru SRI VENKATESHWARA COLLEGE OF ENGINEERING BENGALURU PROJECT REVIEW-One PID:2023P064 PRANAVA SWAROOP N - 1VE20CS115 PRANAV SASTRY – 1VE20CA015 KAUSTHUB RAO MOHITE – 1VE20CS061 ADITYA – 1VE20CA002 “A DEEP LEARNING APPROACH FOR ELECTRONIC WASTE DETECTION, CLASSIFICATION AND SORTING” PROJECT TITLE PRESENTED BY- Department of CSE and CSE-AI SRI VENKATESHWARA COLLEGE OF ENGINEERING Vidyanagar, Bengaluru- 562157

2. CONTENTS Introduction Problem Statement Significance of the Study Objectives of the Study Literature Survey Gaps Identified Methodology User Interface References

3. introduction The rapid growth of technology, upgradation of technical innovations and a high rate of obsolescence in the electronics industry have led to one of the fastest growing waste streams in the world which consist of end of life electrical and electronic equipment products. These discarded electronic items, which include old computers, mobile phones, electronic accessories, and more, pose a dual challenge: environmental and economic. Improper disposal of e-waste not only contributes to environmental pollution but also results in the loss of valuable resources contained within these electronic devices.

4. This project seeks to address the critical need for a more efficient and precise approach to electronic waste management, By leveraging the power of deep learning and computer vision, it aims to develop a solution that can automatically detect, classify, and sort different types of electronic waste items. Deep learning approaches can be instrumental in addressing the challenges of e-waste detection, classification, and sorting. Deep learning techniques, which are a subset of machine learning, leverage artificial neural networks to automatically learn and extract complex patterns and features from data.

5. PROBLEM STATEMENT This system aims to improve the efficiency and accuracy of e-waste recycling by using advanced image recognition and machine learning techniques, ultimately reducing the environmental impact of improper e-waste disposal . Adapting to evolving e-waste products and materials over time. Meeting the requirements for real-time or high-throughput processing in recycling facilities and minimizing false positives and false negatives in the detection and classification process. Nonetheless, several significant challenges must be tackled for a sustainable e-waste management system, including: Inefficient Recycling Practices Lack of Recycling Accountability

6. SIGNIFICANCE OF STUDY The study's significance lies in its potential to reduce environmental harm from electronic waste (e-waste) by improving recycling, resource recovery, and regulatory compliance. It also enhances safety, supports a circular economy, and raises public awareness about responsible e-waste disposal. This study is significant for the following reasons: Scalability and Speed: E-waste management processes need to be scalable and efficient to handle large volumes of waste quickly. Developing automated systems that can process e-waste at a high rate is crucial. Resource Recovery: To make e-waste management sustainable, it is essential to recover valuable materials from electronic waste. However, this requires efficient sorting and processing to maximize resource recovery. Human Health and Safety: Workers involved in e-waste sorting and recycling face potential health risks from exposure to hazardous materials. Proper safety measures and training are necessary to protect the workforce.

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8. OBJECTIVES The primary objectives of this project are as follows: To develop a deep learning model for the detection of electronic devices within a given dataset. To implement a classification system to identify the type and category of detected electronic devices and sorting mechanism based on the classification results to optimize recycling processes. To improve scalability and speed of deep learning models that can process large volumes of e-waste data quickly, making them suitable for high-throughput e-waste management processes. To enhance the deep learning model for it to continuously adapt to changing e-waste materials and components, ensuring that the system remains effective as electronic technology evolves.

9. Literature Survey 9 Paper Details Pros Cons Concept Used Application of deep learning object classifier to improve e-waste collection planning [ Piotr Nowakowski , Teresa Pamuła ] 2020 Efficient classification technique using DIP & CNN Training Methodology used Scalability Computational resources CNN An Approach of Classifying Waste Using Transfer Learning Method [Zian Md Afique Amin, Khan Nasik Sami, Raini Hassan] 2021 High Accuracy High level Architecture Dataset Compatibility Legacy Methods Deep learning methodology Application of artificial intelligence to enhance collection of E-waste [ AV Shreyas Madhav , Raghav Rajaraman, S Harini and Cinu C Kiliroor ] 2021 Training methods VGG16 & RESNET 50 First of its kind attempt, especially in India Complex Implementation Core Implementation

10. Gaps Identified 20XX Pitch Deck 10 High-level classification requires a larger volume of dataset and trainable methods to be explored Computational resource and trainability Cost on any physical and onsite implementation Smart phone dependency

11. data YOU NEED TO ADD YOUR OWN BULLET POINTS train YOU NEED TO ADD YOUR OWN BULLET POINTS eval YOU NEED TO ADD YOUR OWN BULLET POINTS deploy YOU NEED TO ADD YOUR OWN BULLET POINTS fkoff YOU NEED TO ADD YOUR OWN BULLET POINTS methodology

12. Mobile Application

13. Overview and progress of the application 20XX Pitch Deck 13

14. SOURCES AND REFERENCES [1] E waste in India – RESEARCH UNIT (LARRDIS) RAJYA SABHA SECRETARIAT NEW DELHI, JUNE, 2011 [2] https://cleanriver.com/blog-what-is-e-waste-electronic-waste-disposal/ [3] https://www.geeksforgeeks.org/what-is-e-waste/ [4] Bai J, Lian S, Liu Z, et al. (2018) Deep learning based robot for automaticallypicking up garbage on the grass. IEEE Transactions on ConsumerElectronics 64: 382–389. [5] Chu Y, Huang C, Xie X, et al. (2018) Multilayer hybrid deep-learning methodfor waste classification and recycling. Computational Intelligence andNeuroscience 2018: 5060857. [6] Hana D, Liu Q and Fan W (2017) A new image classification method usingCNN transfer learning and web data augmentation. Expert Systems withApplications 95: 43–56. [7] Pamintuan M, Mantiquilla SM, Reyes H, et al. (2019) i -BIN: An intelligenttrash bin for automatic waste segregation and monitoring system. In:2019 IEEE 11th international conference on humanoid, nanotechnology,information technology, communication and control, environment, andmanagement (HNICEM), Laoag, 29 November–1 December, pp.1–5.New York: IEEE.

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