MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices

2026-07-07 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Expert, quick

Summary

MiLSD, a novel micro line-segment detector, is introduced for resource-constrained microcontrollers (MCUs) in applications like visual SLAM and 3D reconstruction. This detector aims to maximize accuracy under a sub-megabyte memory budget, addressing the limitation of existing deep learning methods that exceed low-cost MCU capacities. MiLSD systematically compares three output representations within a compact fully-convolutional backbone, finding that the F-Clip center-with-length-and-angle formulation is most effective for small model sizes. The research demonstrates that 8-bit quantization maintains full-precision performance, while 4-bit quantization significantly degrades angle regression, with only partial recovery via quantization-aware training. Incorporating inference enhancements such as sub-pixel decoding, test-time augmentation, and a lightweight verifier, MiLSD improves sAP10 on ShanghaiTech Wireframe from 10.6 (25k parameters, 0.25 MB) to 24.1 within a 1 MB activation budget.

Key takeaway

For Embedded Vision Engineers deploying line-segment detection on microcontrollers, MiLSD offers a validated approach to achieve high accuracy within strict memory limits. You should prioritize 8-bit quantization and the F-Clip center-with-length-and-angle formulation to optimize performance. Consider integrating sub-pixel decoding and test-time augmentation to maximize sAP10 scores on devices with a 1 MB activation budget. This research provides a clear trade-off map for your embedded vision system design.

Key insights

MiLSD achieves high line-segment detection accuracy on MCUs by optimizing representations and quantization within a sub-megabyte memory budget.

Principles

• F-Clip center-with-length-and-angle excels at small model sizes.
• 8-bit quantization preserves performance for line-segment detection.
• 4-bit quantization degrades angle regression significantly.

Method

MiLSD employs a compact fully-convolutional backbone, systematically comparing output representations. It integrates sub-pixel decoding, test-time augmentation, and a lightweight verifier for inference, mapping accuracy-memory trade-offs.

In practice

• Use F-Clip representation for compact line detectors.
• Apply 8-bit quantization for MCU-level deployment.
• Incorporate sub-pixel decoding for enhanced inference.

Topics

• Line Segment Detection
• Embedded Vision
• Microcontrollers
• Model Quantization
• Resource-Constrained Devices
• Deep Learning Inference

Best for: Research Scientist, AI Scientist, AI Hardware Engineer, Computer Vision Engineer

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