The Development of Real-Time Mobile Garbage Detection Using Deep Learning - Haris Imam Karim Fathurrahman; Alfian Ma'arif; Li-Yi Chin (Universitas Ahmad Dahlan, 2021)
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).