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Efficient Multitask Onboard Vision Sensing for Open-Pit Mining Advanced Driver Assistance System with

Maximiliano Vélez1, Claudio Urrea1

  • 1Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170124, Chile.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
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This summary is machine-generated.

This study introduces an Adaptive Clockwork (A-CW) inference scheme for mining Advanced Driver Assistance Systems (ADASs). The A-CW scheme significantly boosts processing speed for real-time drivable-area segmentation and steering prediction in open-pit mines.

Area of Science:

  • Computer Vision
  • Robotics
  • Autonomous Systems

Background:

  • Heavy mining trucks rely on Advanced Driver Assistance Systems (ADASs) for navigation in unstructured open-pit mine environments.
  • Existing perception models for these systems require high accuracy and speed due to unmarked haul roads.

Purpose of the Study:

  • To develop a fast and accurate video-based multitask pipeline for a mining Driver Support System (DSS).
  • To introduce an Adaptive Clockwork (A-CW) inference scheme to optimize temporal redundancy exploitation without additional video annotation.

Main Methods:

  • A single BiSeNetV1 network was employed for simultaneous drivable-area segmentation and steering-direction classification.
  • An Adaptive Clockwork (A-CW) inference scheme was proposed, dynamically refreshing the context path based on classification output keyframes.
Keywords:
ADASadaptive temporal inferencedrivable area segmentationdriver support systemmultitask deep learningonboard vision sensorsopen-pit miningsteering classificationvehicular sensingvideo semantic segmentation

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Main Results:

  • The A-CW scheme achieved 94.70% road-class IoU and 73.68% Top-1 Accuracy on an unseen route.
  • GPU-only throughput increased from 55 FPS to 168.01 FPS, with end-to-end processing at approximately 37.5 FPS.

Conclusions:

  • The A-CW inference scheme enhances the efficiency of perception models for mining ADASs.
  • This approach effectively balances accuracy and speed for real-time applications in challenging mining conditions.