uHairDet:面向雄激素性脱发诊断的仿真毛发图像生成与检测方法

doi:10.11896/jsjkx.251100057

Abstract

Abstract: The clinical diagnosis of Androgenetic Alopecia(AGA) relies heavily on manual hair counting,a process that is time-consuming,subjective,and limits diagnostic efficiency.While automated hair recognition technology holds great promise,it faces challenges due to fine hair structures and a lack of pixel-level annotated data.To address this,this paper proposes uHairDet,a novel framework that minimizes dependency on manual annotations by leveraging synthetically generated data.The proposed approach consists of three key components:1)a hair synthesis with built-in annotations data generator(HBDG) for creating structurally plausible hair images with pixel-level labels;2)a structure-stable style-transfer GAN(BS-GAN) incorporating a semantic-aware adaptive error correction loss(HEE Loss) to enhance structural consistency;3)an FCOS+PSC detection model based on a Mean-Teacher framework,trained with a new oriented bounding box(OBB) annotation paradigm tailored for hairs to preserve critical information.Results demonstrate that the proposed method,requiring no manual labels,achieves a 56.9% AP,significantly outperforming baseline models and establishing a new paradigm for intelligent assisted diagnosis in hair-related disorders.

Key words: Hair detection and counting, Rotation detection, Domain adaptation, Hair synthesis, Loss function

CLC Number:

TP391

CHEN Qi, CHEN Xingkai, ZHANG Huihuang, SU Yiping, HU Haigen. uHairDet:Method for Synthetic Hair Image Generation and Detection in Androgenetic AlopeciaDiagnosis[J].Computer Science, 2026, 53(5): 207-217.

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