Computer Vision Based Two-stage Waste Recognition-Retrieval Algorithm for Waste Classification

The main objective of this study is to classify domestic waste via computer vision and sort it automatically according to the four-category regulation. A novel two-stage Waste Recognition-Retrieval algorithm (W2R) is proposed. Its first stage was to train a Recognition Model (RegM) recognizing waste into one of thirteen subcategories. The second stage was to construct the Recognition-Retrieval Model (RevM) classifying the recognized subcategory into one of four categories. Meanwhile, a one-stage waste Classification Model (ClfM) was trained as a comparison. Both best-performing models were selected and installed respectively onto the automatic sorting machine for contrast experiment classifying a set of waste. The machine consisted of three main modules: the Computer-Vision Module, the Sorting Module, and the Customized Module. It was also a platform for data collection. Ten participants also classified and sorted the same set of waste in the experiment of Manual Sorting (MS). The experimental results show that the average accuracy of the RevM, 94.71% ± 1.69, was significantly higher than that of the ClfM-VGG, 69.66% ± 3.43, and that of the MS, 72.50% ± 11.37. The first stage of the Waste Recognition-Retrieval algorithm (W2R) is to train a Recognition Model (RegM) recognizing waste into one of thirteen subcategories. The second stage is to construct the Recognition-Retrieval Model (RevM) classifying the recognized subcategory into one of four categories [Display omitted] .