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Weakly perceived object detection based on an improved CenterNet.

Jing Zhou1, Ze Chen1, Xinhan Huang2

  • 1School of Artificial Intelligence, Jianghan University, Wuhan 430056, China.

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|January 19, 2023
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Summary
This summary is machine-generated.

This study introduces an improved CenterNet model to enhance object detection accuracy for small or weakly perceived objects in complex scenes. The enhanced model significantly boosts detection precision, particularly for vehicles, pedestrians, and small objects in datasets like KITTI and COCO.

Keywords:
CenterNetanchor-freeattention mechanismmulti-scale feature enhancementobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep neural network object detection is crucial for autonomous driving and robotics.
  • Detecting small or weakly perceived objects in complex scenes remains a challenge due to limited features and reduced accuracy.

Purpose of the Study:

  • To enhance the feature representation of weakly perceived objects for improved detection accuracy in complex environments.
  • To develop an improved CenterNet model capable of better identifying small and challenging objects.

Main Methods:

  • Replaced ResNet50 with ResNext50 as the backbone for superior feature extraction.
  • Integrated lateral connection structure and dilated convolution to enrich features and enlarge receptive fields.
  • Applied an attention mechanism in the detection head to emphasize key object information.

Main Results:

  • The improved CenterNet demonstrated increased average precision for vehicles and pedestrians (5.37%) on the KITTI dataset.
  • Achieved a 9.30% increase in average precision for weakly perceived pedestrians on KITTI.
  • Showcased a 7.4% improvement in average precision for small objects (AP_S) on the COCO dataset.

Conclusions:

  • The proposed enhancements to CenterNet significantly improve the detection accuracy of weakly perceived and small objects.
  • The model proves effective in complex scenes, offering a valuable advancement for autonomous systems and intelligent robotics.