Intelligent management of waste bins in indoor public places based on waste detection and recognition

Abstract

Due to the lack of convenient waste bins nearby, many people choose to litter indiscriminately in indoor public places. Dirty floors not only diminish the shopping experience of customers, but also increase potential risks to pedestrian safety. For the intelligent management of waste bins in indoor public places, this paper proposes a novel solution based on automatic waste detection and recognition, which helps to minimize littering and improve the recycling efficiency of indoor waste. This solution mainly uses a deep learning model to detect and recognize waste items, which can effectively record the distribution of waste and provide a basis for the reasonable placement of waste bins. Specifically, we construct a high-quality indoor waste image dataset for waste detection, which can provide an effective data source for model optimization of this task. This dataset is collected from three common public places, including hospitals, supermarkets, and subway stations. At the same time, it contains four waste categories, 4000 color images, and 6968 box-level annotations. In addition, we propose a novel feature decoupling network for indoor waste detection and recognition, which disentangles specific features for different vision tasks. On the one hand, it uses a recognition enhancement module to generate discriminative feature maps with more semantic information. On the other hand, it introduces a detection enhancement module to output rich feature maps with more detail information. As plug-and-play modules, these two modules are suitable for different networks. Relevant experimental results show that our method is competitive with other representative object detection models and can achieve consistent performance improvements on distinct models. In general, our solution provides new insights into indoor waste bin management.