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| Abstract : Universiti Pendidikan Sultan Idris |
| This paper proposes a system for automatic detection of litter and garbage dumps in CCTV feeds with the help of deep learning implementations. The designed system named Greenlock scans and identifies entities that resemble an accumulation of garbage or a garbage dump in real time and alerts the respective authorities to deal with the issue by locating the point of origin. The entity is labelled as garbage if it passes a certain similarity threshold. ResNet-50 has been used for the training purpose alongside TensorFlow for mathematical operations for the neural network. Combined with a pre-existing CCTV surveillance system, this system has the capability to hugely minimize garbage management costs via the prevention of formation of big dumps. The automatic detection also saves the manpower required in manual surveillance and contributes towards healthy neighborhoods and cleaner cities. This article is also showing the comparison between applied various algorithms such as standard TensorFlow, inception algo and faster-r CNN and Resnet-50, and it has been observed that Resnet-50 performed with better accuracy. The study performed here proved to be a stress reliever in terms of the garbage identification and dumping for any country. At the end of the article the comparison chart has been shown 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved. |
| References |
Ying Liu and Zhishan Ge, “Research on automatic Garbage Detection System Based on Deep Learning and Narrowband Internet of Things”, 2018. Zhong-Qiu Zhao and Peng Zheng, “Object Detection with Deep Learning: A Review”, 2017. Huang, Jonathan & Rathod, Vivek & Sun, Chen & Zhu,, “Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors”, 2017 Nurminen, Jukka & Rainio, Kari & Numminen, Jukka-Pekka, “Object Detection in Design Diagrams with Machine Learning.”, 2018. Anitya & Kumar, Akhilesh & Bhushan, Vinayak , “World of intelligence defense object detection “machine learning”, 2018. Chen, Chunlin & Ling, Qiang,” Adaptive Convolution for Object Detection”, 2019. Huang, Lili & Wu, Hefeng & Li, Guanbin & Wang, Qing, “Instructionguided object detection.”, 2019. Kharinov, Mikhail & Buslavsky, A, “Object Detection in Color Image”, 2019. Liu, Ying & Ge, Zhishan & Lv, Guoyun & Wang, Shikai., “Research on Automatic Garbage Detection System Based on Deep Learning and Narrowband Internet of Things.”, 2018. Hu, Guo Qiang, Jing Chang Huang, Jun Chi Yan, and Jun Zhu. "Object detection." U.S. Patent 10,706,530, issued July 7, 2020. Adrian Rosebrook, “Object detection with deep learning and OpenCV” Lakshmi, V. Srinithi Santhana, et al. "Smart garbage alert system using machine learning." Int. J. Eng. Appl. Sci. Technol 5 (2020): 487-489. Raccon Dataset, GitHub.com Singh, Shubhendu, Kushal Samir Mehta, Nishant Bhattacharya, Jyotsna Prasad, S. Kaala Lakshmi, K. V. Subramaniam, and Dinkar Sitaram. "Identifying uncollected garbage in urban areas using crowdsourcing and machine learning." In 2017 IEEE Region 10 Symposium (TENSYMP), pp. 1-5. IEEE, 2017. Joshi, Jetendra, Joshitha Reddy, Praneeth Reddy, Akshay Agarwal, Rahul Agarwal, Amrit Bagga, and Abhinandan Bhargava. "Cloud computing based smart garbage monitoring system." In 2016 3rd International Conference on Electronic Design (ICED), pp. 70-75. IEEE, 2016. Geiger, R. Stuart, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, and Jenny Huang. "Garbage in, garbage out? Do machine learning application papers in social computing report where human-labeled training data comes from?." In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 325-336. 2020. Hoque, Mohammad Akidul, Mrittika Azad, and Md Ashik-Uz-Zaman. "IoT and Machine Learning Based Smart Garbage Management and Segregation Approach for Bangladesh." 2019 2nd International Conference on Innovation in Engineering and Technology . IEEE, 2019. Shamin, N., P. Mohamed Fathimal, R. Raghavendran, and Kamalesh Prakash. "Smart garbage segregation & management system using Internet of Things (IoT) & Machine Learning (ML)." In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pp. 1-6. IEEE, 2019.. Baby, Cyril Joe, Harvir Singh, Archit Srivastava, Ritwik Dhawan, and P. Mahalakshmi. "Smart bin: An intelligent waste alert and prediction system using machine learning approach." In 2017 international conference on wireless communications, signal processing and networking (WiSPNET), pp. 771-774. IEEE, 2017. DeBrusk, Chris. "The risk of machine-learning bias (and how to prevent it)." MIT Sloan Management Review (2018). Kim, In Kee, Sai Zeng, Christopher Young, Jinho Hwang, and Marty Humphrey. "iCSI: A cloud garbage VM collector for addressing inactive VMs with machine learning." In 2017 IEEE International Conference on Cloud Engineering (IC2E), pp. 17-28. IEEE, 2017. Wang, Ying, and Xu Zhang. "Autonomous garbage detection for intelligent urban management." MATEC Web of Conferences. Vol. 232. EDP Sciences, 2018. Park, Jung Kyu, and Jaeho Kim. "A method for reducing garbage collection overhead of SSD using machine learning algorithms." 2017 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2017. Gan, Ji Sheng. Automated Garbage Classification and Sorting System using Machine Learning. Diss. Tunku Abdul Rahman University College, 2020. Sharma, Akhilesh K., Kamaljit I. Lakhtaria, Avinash Panwar, and Santosh Vishwakarma. "An analytical approach based on self organized maps (SOM) in Indian classical music raga clustering." In 2014 Seventh International Conference on Contemporary Computing (IC3), pp. 449-453. IEEE, 2014. Sharma, Akhilesh K., Avinash Panwar, Prasun Chakrabarti, and Santosh Vishwakarma. "Categorization of ICMR Using feature extraction strategy and MIR with ensemble learning." Procedia Computer Science 57 (2015): 686-694. Sharma, Akhilesh K., Avinash Panwar, and Prasun Chakrabarti. "Analytical approach on Indian classical raga measures by feature extraction with EM and Naive Bayes." International Journal of Computer Applications 107.6 (2014). Sharma, Akhilesh K., and Prakash Ramani. "Rigorous data analysis and performance evaluation of Indian classical raga using RapidMiner." Soft Computing: Theories and Applications. Springer, Singapore, 2018. 97-106. Sharma, Akhilesh Kumar, Gaurav Aggarwal, Sachit Bhardwaj, Prasun Chakrabarti, Tulika Chakrabarti, Jemal H. Abawajy, Siddhartha Bhattacharyya, Richa Mishra, Anirban Das, and Hairulnizam Mahdin. "Classification of Indian classical music with time-series matching deep learning approach." IEEE Access 9 (2021): 102041-102052. |
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