Pengenalan Personal Menggunakan Citra Tampak Atas pada Lingkungan Cashierless Strore

Prastowo, Bambang Nurcahyo; Putro, Nur Achmad Sulistyo; Dhewa, Oktaf Agni; Yusuf, Ach Maulana Habibi

doi:10.24002/jbi.v10i1.1779

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“…Object detection is one of the most challenging issues in the field of computer vision [4]. The development of increasingly rapid computer vision is shown by the emergence of new methods and algorithms so that the process of object recognition/identification becomes faster and more accurate [5]. Object detection is related to identifying real-world objects such as people, animals, and other objects.…”

Section: Introductionmentioning

confidence: 99%

Performance of Faster R-CNN to Detect Plastic Waste

2020

IJATCSE

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Waste production in Indonesia reaches more than 65 million tons per year, which is a problem that has not been appropriately resolved. The government and organization must raise awareness of waste because improper waste management can cause pollution. The organization can manage waste by recycling to solve this problem. However, these efforts have encountered obstacles because public awareness of sorting waste is still deficient. Therefore, we need a sorting system for organic and inorganic waste. Reducing waste problems can be started by separating plastic waste from organic waste. This study aims to detect plastic waste and other separate plastic waste from organic waste. This plastic waste detection system will extract digital image features to detect plastic waste using the deep learning method. Deep learning is used because it is proven to have good performance. Faster Regional Convolutional Neural Network (Faster R-CNN) is a deep learning technique recently developed to recognize and classify computer vision. Faster R-CNN is an algorithm that utilizes the Convolutional Neural Network (CNN) in the object detection process. This study will detect and localize plastic waste in the image using the Faster R-CNN algorithm by utilizing the TensorFlow object detection framework. The system implements four network architecture models, namely Inception Resnet V2, Inception V2, Resnet 101, and Resnet 50. Based on the experiments that have been conducted, Faster R-CNN can provide good performance by obtaining an F1 Score of 93% on the Inception Resnet V2.

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Section: Introductionmentioning

confidence: 99%

Performance of Faster R-CNN to Detect Plastic Waste

2020

IJATCSE

View full text Add to dashboard Cite

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Pengenalan Personal Menggunakan Citra Tampak Atas pada Lingkungan Cashierless Strore

Cited by 1 publication

References 0 publications

Performance of Faster R-CNN to Detect Plastic Waste

Performance of Faster R-CNN to Detect Plastic Waste

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