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.
Highlights
Due to the constructions, renovations, and demolition, massive amounts of construction and demolition (C&D) wastes are generated around the world
Vision-based solid waste sorting systems are a fundamental module in the C&D waste recycling industry.[1]
We propose a deep multi-scale fusion cross modal RGB-D object detection network
Summary
Renovations, and demolition, massive amounts of construction and demolition (C&D) wastes are generated around the world. In the output layer.[7] Such methods achieve lower accuracy rates but are much faster than two-stage object detectors. All of these approaches assume the availability of large annotated data sets and clear RGB images. The color information of the images will be seriously loss, and the accuracy of recognition will be greatly degraded. Due to these two reasons, the mainstream CNN-based detection methods are unable to achieve the perfect accuracy of solid waste detection. We propose a deep multi-scale fusion cross modal RGB-D object detection network. Experimental results of the proposed methods are integrated into a real-life robotic system
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