Performance of Faster R-CNN to Detect Plastic Waste

Abstract

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.

Solid Waste Detection and Recognition using Faster RCNN

“…Faster R-CNN (9) is an advanced object detection model that enhances Fast R-CNN by incorporating a region proposal network (RPN) (10) with the CNN model. The RPN efficiently shares full-image convolutional features with the detection network, https://www.indjst.org/ resulting in nearly cost-free region proposals.…”