A novel detection fusion network for solid waste sorting

Abstract

Vision-based object detection technology plays a very important role in the field of computer vision. It is widely used in many machine vision applications. However, in the specific application scenarios, like a solid waste sorting system, it is very difficult to obtain good accuracy due to the color information of objects that is badly damaged. In this work, we propose a novel multimodal convolutional neural network method for RGB-D solid waste object detection. The depth information is introduced as the new modal to improve the object detection performance. Our method fuses two individual features in multiple scales, which forms an end-to-end network. We evaluate our method on the self-constructed solid waste data set. In comparison with single modal detection and other popular cross modal fusion neural networks, our method achieves remarkable results with high validity, reliability, and real-time detection speed.