The Development of Real-Time Mobile Garbage Detection Using Deep Learning

Abstract

The problem of garbage in the world is a serious issue that must be solved. Good garbage management is a must for now and in the future. Good garbage management is accompanied by a system of classification and sorting of garbage types. This study aims to create a mobile-based application that can select the type of garbage and enter the garbage data into a database. The database used is a Google SpreadSheet that will accommodate data from the output issued by the garbage detection mobile application. The image data used in this study amounted to 10108 images and was divided into six different garbage classes. This study uses a deep learning platform called densenet121 with an accuracy rate of 99.6% to train the image data. DenseNet121 has been modified and added an optimization based on a genetic algorithm. The genetic algorithm applied in the optimization uses four generations. The model resulting from the training of the two approaches is converted into a model that mobile applications can access. The mobile application based on a deep learning model accommodates the detection data of the type of garbage, the level of detection accuracy, and the GPS location of the garbage. In the final experiment of the mobile application, the delay time in sending data was very fast, which was less than one second (0.86s).

Application research of image classification algorithm based on deep learning in household garbage sorting

“…The method presented in Ref. [ 17 ] uses the DenseNet model for real-time classification of various types of garbage using mobile phones. The proposed DenseNet model consists of 4 dense blocks, and the number of neurons in each block is adjusted using a genetic algorithm.…”

Comparative Study of ML Algorithms for Garbage Classification

“…For example, the paper proposes a garbage classi cation method based on an improved VGG16 transfer learning model, aiming to improve the learning ability of a new eld through information from related elds [28]. Several garbage classi cation approaches have been used in recent years, including arti cial intelligence implementations and fuzzy approaches [29]. The paper investigates different models based on convolutional neural networks (CNNs) for garbage classi cation.…”

Indonesian Waste Database: Smart Mechatronics System

“…Much research has been conducted in recent years to promote the use of technology in trash management. The Internet of Things [19]- [21], robotics [22]- [25], automation [26]- [28], and artificial intelligence [29]- [31] are some of the technologies that may be employed in waste management. The Internet of Things can play a role in the management of waste information in the environment as well as a centralized means of delivering data.…”

“…In recent years, artificial intelligence has been used to identify garbage categories, detect rubbish objects, and anticipate waste. By applying computer-based [32], mobile [31], and web-based applications [33], artificial intelligence may also be employed as an integrated system. A database that can be utilized as a learning system is essential in artificial intelligence.…”