Vol.68, No.2, 2021, pp.2221-2229, doi:10.32604/cmc.2021.017073
Real-Time Recognition and Location of Indoor Objects
  • Jinxing Niu1,*, Qingsheng Hu1, Yi Niu1, Tao Zhang1, Sunil Kumar Jha2
1 Institute of Mechanics, North China University of Water Resources and Electric Power, Zhengzhou, 450011, China
2 IT Fundamentals and Education Technologies Applications, University of Information Technology and Management in Rzeszow, Rzeszow, 100031, Poland
* Corresponding Author: Jinxing Niu. Email:
Received 18 January 2021; Accepted 20 February 2021; Issue published 13 April 2021
Object recognition and location has always been one of the research hotspots in machine vision. It is of great value and significance to the development and application of current service robots, industrial automation, unmanned driving and other fields. In order to realize the real-time recognition and location of indoor scene objects, this article proposes an improved YOLOv3 neural network model, which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network, which is applied to the detection and recognition of objects in indoor scenes. In this article, RealSense D415 RGB-D camera is used to obtain the RGB map and depth map, the actual distance value is calculated after each pixel in the scene image is mapped to the real scene. Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene. More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement. The running time of objects detection and recognition is reduced to less than half of the original. This improved network has a certain reference value for practical engineering application.
Object recognition; improved YOLOv3 network; RGB-D camera; object location
Cite This Article
J. Niu, Q. Hu, Y. Niu, T. Zhang and S. K. Jha, "Real-time recognition and location of indoor objects," Computers, Materials & Continua, vol. 68, no.2, pp. 2221–2229, 2021.
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