EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection

Hiroki Nakamura; Hiroto Iino; Masashi Okada; Tadahiro Taniguchi

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arXiv · 2602.16238

EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection

Hiroki Nakamura·Hiroto Iino·Masashi Okada·Tadahiro Taniguchi

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Abstract

We propose EasyControlEdge, adapting an image-generation foundation model to edge detection. In real-world edge detection (e.g., floor-plan walls, satellite roads/buildings, and medical organ boundaries), crispness and data efficiency are crucial, yet producing crisp raw edge maps with limited training samples remains challenging. Although image-generation foundation models perform well on many downstream tasks, their pretrained priors for data-efficient transfer and iterative refinement for high-frequency detail preservation remain underexploited for edge detection. To enable crisp and data-efficient edge detection using these capabilities, we introduce an edge-specialized adaptation of image-generation foundation models. To better specialize the foundation model for edge detection, we incorporate an edge-oriented objective with an efficient pixel-space loss. At inference, we introduce guidance based on unconditional dynamics, enabling a single model to control the edge density through a guidance scale. Experiments on BSDS500, NYUDv2, BIPED, and CubiCasa compare against state-of-the-art methods and show consistent gains, particularly under no-post-processing crispness evaluation and with limited training data.

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BibTeX RIS

Hiroki Nakamura, Hiroto Iino, Masashi Okada, Tadahiro Taniguchi. 2026-02-18. EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection. https://arxiv.org/abs/2602.16238

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