Real-Time Deep ConvNet-Based Vehicle Detection Using 3D-LIDAR Reflection Intensity Data

  • Alireza Asvadi
  • Luis Garrote
  • Cristiano Premebida
  • Paulo Peixoto
  • Urbano J. Nunes
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 694)


This paper addresses the problem of vehicle detection using a little explored LIDAR’s modality: the reflection intensity. LIDAR reflection measures the ratio of the received beam sent to a surface, which depends upon the distance, material, and the angle between surface normal and the ray. Considering a 3D-LIDAR mounted on board a robotic vehicle, which is calibrated with respect to a monocular camera, a Dense Reflection Map (DRM) is generated from the projected sparse LIDAR’s reflectance intensity, and inputted to a Deep Convolutional Neural Network (ConvNet) object detection framework for the vehicle detection. The performance on the KITTI is superior to some of the approaches that use LIDAR’s range-value, and hence it demonstrates the usability of LIDAR’s reflection for vehicle detection.


Vehicle detection 3D-LIDAR reflection Deep learning 



This work has been supported by “AUTOCITS - Regulation Study for Interoperability in the Adoption of Autonomous Driving in European Urban Nodes” - Action number 2015-EU-TM-0243-S, co-financed by the European Union (INEA-CEF); and FEDER through COMPETE 2020, Portugal 2020 program under grant UID/EEA/00048/2013.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Alireza Asvadi
    • 1
  • Luis Garrote
    • 1
  • Cristiano Premebida
    • 1
  • Paulo Peixoto
    • 1
  • Urbano J. Nunes
    • 1
  1. 1.Department of Electrical and Computer Engineering, Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal

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