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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Efficient Object Detection Using Semantic Region of Interest Generation with Light-Weighted LiDAR Clustering in

Dongkyu Jung1, Taewon Chong2,3, Daejin Park1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
Summary

This study introduces a novel method for 3D object detection using lightweight systems. It enhances 2D convolutional neural network (CNN) object detection by leveraging 3D data characteristics, improving accuracy and reducing processing time.

Keywords:
LiDAR sensorconvolution neural network (CNN)object detectionpoint cloudsemantic detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Convolutional Neural Networks (CNNs) are widely used for object detection in 3D data.
  • 3D data-based algorithms offer stability but demand significant computational resources, limiting their use in embedded systems.
  • Existing 2D CNNs struggle with lighting variations and require complex processing for 3D data.

Purpose of the Study:

  • To develop a computationally efficient method for 3D object detection on lightweight embedded systems.
  • To enhance the accuracy of 2D CNN object detection by integrating 3D data characteristics.
  • To overcome the limitations of traditional 3D and 2D object detection methods in varying environmental conditions.

Main Methods:

  • Preprocessing LiDAR point cloud data and employing clustering for object separation.
  • Extracting physical characteristics from 3D data for semantic detection using machine learning classifiers.
  • Generating 2D image regions from detected 3D objects to perform 2D CNN object detection, bypassing bounding box tracking.

Main Results:

  • The proposed model achieved 81.84% accuracy using YOLO v5 on an embedded board, surpassing typical models by 1.92%.
  • Demonstrated superior performance in non-optimal lighting conditions, with 47.41% accuracy in high brightness (+40%) and 54.12% in low brightness (-40%).
  • Achieved significant accuracy improvements (8.97% and 13.58% respectively) over general models in challenging lighting.

Conclusions:

  • The proposed method effectively utilizes 3D data characteristics to enhance 2D CNN object detection accuracy in resource-constrained environments.
  • The approach offers a lighter system alternative to purely 3D data-based detection algorithms.
  • The technique shows robustness in varying lighting conditions and reduces execution time, making it suitable for real-world embedded applications.