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Mobip: a lightweight model for driving perception using MobileNet.

Minghui Ye1, Jinhua Zhang1

  • 1School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China.

Frontiers in Neurorobotics
|December 19, 2023
PubMed
Summary

We developed Mobip, a fast, lightweight multi-task network for autonomous driving perception. It efficiently handles object detection, drivable area segmentation, and lane line detection, crucial for self-driving car decision-making.

Keywords:
lightweight networkmulti-task learningself-drivingsemantic segmentationtraffic object detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Visual perception models are essential for autonomous driving systems, enabling self-driving cars to interpret traffic scenes.
  • Accurate and efficient perception is key to safe navigation and decision-making in complex driving environments.

Purpose of the Study:

  • To propose a lightweight multi-task network, named Mobip, for simultaneous traffic object detection, drivable area segmentation, and lane line detection.
  • To achieve high inference speed without compromising performance on critical perception tasks.

Main Methods:

  • Developed a multi-task network (Mobip) featuring a shared encoder (MobileNetV2 backbone) and two decoders for combined detection and segmentation.
  • Implemented an efficient multi-task architecture to optimize feature extraction and task-specific processing.

Main Results:

  • Mobip achieved an inference speed of 58 FPS on an NVIDIA Tesla V100 GPU.
  • The model demonstrated competitive performance across object detection, drivable area segmentation, and lane line detection on the BDD100K dataset.
  • Ablative studies confirmed the effectiveness and efficiency of the proposed multi-task architecture.

Conclusions:

  • The lightweight Mobip network offers a highly efficient solution for multi-task visual perception in autonomous driving.
  • The proposed architecture balances inference speed and performance, making it suitable for real-time applications.
  • Mobip provides a viable approach for enhancing the decision-making capabilities of self-driving cars.