Implementation Convolutional Neural Network for Visually Based Detection of Waste Types

Bayu Yasa  Wedha; Ira Diana  Sholihati; Sari  Ningsih

doi:10.47709/cnahpc.v6i1.3427

Authors

Bayu Yasa Wedha Prodi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional Jakarta
Ira Diana Sholihati Prodi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional Jakarta
Sari Ningsih Prodi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional Jakarta

DOI:

10.47709/cnahpc.v6i1.3427

Keywords:

Garbage Detection, Convolutional Neural Network, Waste Management, Object Segmentation, Transfer Learning

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Abstract

Waste detection plays an essential role in ensuring efficient waste management. Convolutional Neural Networks are used in visual waste detection to improve waste management. This study uses a data set that covers various categories of waste, such as plastic, paper, metal, glass, trash, and cardboard. Convolutional Neural Networks are created and trained with refined architecture to achieve precise classification results. During the model development stage, the focus is on utilizing transfer learning techniques to implement Convolutional Neural Networks. Utilizing pre-trained models will speed up and improve the learning process by enriching the representation of waste features. By using the information embedded in the trained model, the Convolutional Neural Network can differentiate the specific attributes of various waste categories more accurately. Utilizing transfer learning allows models to adapt to real-world scenarios, thereby improving their ability to generalize and accurately identify waste that may exhibit significant variation in appearance. Combining these methodologies enhances the ability to identify waste in diverse environmental conditions, facilitates efficient waste management, and can be adapted to contemporary needs in environmental remediation. The model evaluation shows satisfactory performance, with a recognition accuracy of about 73%. Additionally, experiments are conducted under authentic circumstances to assess the reliability of the system under realistic circumstances. This study provides a valuable contribution to the advancement of waste detection systems that can be integrated into waste management with optimal efficiency.

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