Spatial-Importance-Based Computation Scheme for Real-Time Object Detection from 3D Sensor Data

Ryo Otsu, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki, Daiki Hasegawa, Toshikazu Furuya

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Three-dimensional (3D) sensor networks using multiple light-detection-and-ranging (LIDAR) sensors are good for smart monitoring of spots, such as intersections, with high potential risk of road-traffic accidents. The image sensors must share the strictly limited computation capacity of an edge computer. To have the computation speeds required from real-time applications, the system must have a short computation delay while maintaining the quality of the output, e.g., the accuracy of the object detection. This paper proposes a spatial-importance-based computation scheme that can be implemented on an edge computer of image-sensor networks composed of 3D sensors. The scheme considers regions where objects exist as more likely to be ones of higher spatial importance. It processes point-cloud data from each region according to the spatial importance of that region. By prioritizing regions with high spatial importance, it shortens the computation delay involved in the object detection. A point-cloud dataset obtained by a moving car equipped with a LIDAR unit was used to numerically evaluate the proposed scheme. The results indicate that the scheme shortens the delay in object detection.

Original languageEnglish
Pages (from-to)5672-5680
Number of pages9
JournalIEEE Access
Publication statusPublished - 2022


  • LIDAR sensor
  • Smart monitoring
  • edge computing
  • object detection
  • point cloud

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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