Deep learning networks for real-time regional domestic waste detection

Waste sorting is highly labor intensive because the wide variety of waste items prohibits automation. More recently, deep learning (DL) and computer vision technology has presented an opportunity to streamline the sorting process, but many important developmental steps remain. If computer vision technology can increase the efficiency of automated waste sorting, this would be beneficial for society and the environment. Accordingly, this study used the You Only Look Once-v3 (Yolo-v3) detection model based on DL to enhance recognition performance of household waste products. TrashNet, a commonly used waste image database, was used to train an initial Yolo-v3 model, however each image used for training only had a single waste object, and this study found that the detection model trained with a single object dataset was not only unsuitable for sorting multiple waste objects, but that this has rarely been addressed in academic literature. It was also discovered that nations and regions will need to develop their own unique databases that reflect the types of waste products found. Samples images need to account for the various appearances and colors and be combined in multiple waste object images when training the system. This paper documents the training and testing of an object detection model suitable for detecting domestic waste specific to Taiwan; however, the approach taken would be of use to other countries seeking to automate waste sorting. To achieve this, it was necessary to compile the Taiwan Recycled Waste Database (TRWD). This was then used to train the Yolo-v3, and the efficiencies of this, versus the standard TrashNet model were compared. Results showed that the TRWD-trained Yolo-v3 achieved mAP @0.5 of 92.12% and could detect waste in real-time. Relative to the TrashNet-trained Yolo-v3, the TRWD counterpart performed better due to the multiple waste objects and more relevant image repository. Further studies are recommended to investigate the effect of combining additional sensors that would enable improved detection of specific wastes. Combining the TRWD-trained Yolo-v3 with a robot system for waste sorting would potentially be another rewarding avenue of research. • Automatic waste detection improves waste recycling efficiency. • Different nations require customized datasets to train Yolo-v3 detection model. • Taiwan recycled waste dataset (TRWD) was expanded to improve detection rates. • Yolo-v3 trained on the TRWD outperformed the same system using TrashNet.