MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices
Line segment detection is key in visual SLAM, 3D reconstruction, and industrial inspection. Deep learning models require several megabytes of memory, exceeding MCU capacity. This work proposes MiLSD, a detector for sub-megabyte budgets. It compares three output representations in a compact backbone, finding F-Clip (center with length and angle) most effective. 8-bit quantization preserves full precision; 4-bit degrades angle regression. With 1 MB activation budget, MiLSD improves sAP10 from 10.6 to 24.1 on ShanghaiTech Wireframe. The paper maps accuracy-memory trade-offs for embedded vision.
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[Submitted on 7 Jul 2026]
Title:MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices
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Abstract:Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection. Recent deep learning methods have greatly improved accuracy, yet even the smallest models require several megabytes of memory, exceeding low-cost MCU capacity. This work investigates the maximum achievable accuracy under a sub-megabyte budget. We propose MiLSD, a detector tailored for MCU-level constraints, and systematically compare three output representations within a compact fully-convolutional backbone. Our study shows that the proposed F-Clip center-with-length-and-angle formulation learns most effectively at small model sizes. We find that 8-bit quantization preserves full-precision performance, while 4-bit quantization causes significant degradation, particularly in angle regression, with quantization-aware training recovering only part of the loss. With a one-megabyte activation budget and inference enhancements including 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 1 MB. Rather than competing with GPU-scale parsers, we map the accuracy memory trade-off across representations, bit-widths, capacities, and post-processing strategies for embedded vision systems.
Comments: 10 pages, 12 figures, 5 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2607.06600 [cs.CV]
(or arXiv:2607.06600v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.06600
arXiv-issued DOI via DataCite
Submission history
From: Parsa Hassani Shariat Panahi [view email] [v1] Tue, 7 Jul 2026 00:04:50 UTC (1,421 KB)
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