Instance-Aware Knowledge Distillation for Semi-Supervised Learning of an On-Board Multi-Task Dense Prediction Model for Collision Avoidance System

2026-06-15 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, quick

Summary

An instance-aware knowledge distillation framework is proposed for semi-supervised learning of an on-board multi-task dense prediction model, specifically designed for collision avoidance systems in edge environments like country clubs. This framework addresses limitations such as limited computational resources, unreliable communication, and high annotation costs for target domain datasets. It generates pseudo labels that mitigate teacher bias by combining domain priors from a teacher model with instance-centric knowledge from foundation models. The resulting lightweight student model is deployed in a collision avoidance system, performing real-time dense prediction tasks to detect frontal obstacles and encode spatial information into controller area network messages for automated guided vehicle operation. Experimental results show the student outperforms the large teacher in instance segmentation while minimizing monocular depth estimation degradation. The student reduces FLOPs by 22.68× and parameters by 14.33×, achieving 6.46 FPS on a low-cost edge device.

Key takeaway

For Computer Vision Engineers developing collision avoidance systems for edge environments, this framework offers a viable path to deploy high-performance models on resource-constrained hardware. You can achieve significant computational reductions, specifically 22.68× FLOPs and 14.33× parameters, while maintaining or improving critical task performance like instance segmentation. Consider adopting instance-aware knowledge distillation to overcome dataset annotation costs and unreliable communication challenges in similar real-time, multi-task dense prediction applications.

Key insights

Instance-aware knowledge distillation improves lightweight models for edge-deployed multi-task dense prediction by mitigating teacher bias.

Principles

• Combine teacher domain priors with foundation model instance knowledge.
• Pseudo-label generation can mitigate teacher model bias.
• Lightweight student models can surpass large teachers in specific tasks.

Method

Generate pseudo labels using domain priors from a teacher and instance-centric knowledge from foundation models, then train a lightweight student model semi-supervisedly.

In practice

• Deploy lightweight models for real-time collision avoidance.
• Use knowledge distillation for resource-constrained edge devices.
• Apply multi-task dense prediction in automated guided vehicles.

Topics

• Knowledge Distillation
• Semi-Supervised Learning
• Collision Avoidance Systems
• Edge Computing
• Multi-Task Learning
• Instance Segmentation

Best for: AI Scientist, Research Scientist, Computer Vision Engineer, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.