Hierarchical Discriminative Feature Refinement for Robust and Lightweight Clothing Detection

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Accurate and efficient clothing detection remains challenging because visually similar garment categories can share appearance cues, while background interference, scale variation, and spatial overlap weaken category-discriminative features. To address these issues, this study proposes a lightweight clothing detector based on YOLO26n with hierarchical discriminative feature refinement. The framework progressively enhances garment representations at the channel, semantic, and multi-scale spatial levels. Specifically, the C3k2-based Channel Feature Enhancement Module (C3k2_CFEM) strengthens cross-channel interaction and adaptively balances local details with contextual information. The C2-based Gated Semantic Attention module (C2GSA) selectively retains task-relevant high-level responses while suppressing redundant contextual interference. The Multi-Scale Dual-Stream Attention Module (MSDSAM) combines complementary foreground–background attention with efficient multi-scale feature interaction. A multi-source clothing dataset containing 11,970 images and 21,504 annotated garment instances across seven categories was used for evaluation. The proposed model achieves 91.6% Precision, 86.2% Recall, 92.6% mAP50, and 74.7% mAP50:95, improving the YOLO26n baseline by 6.6 and 5.8 percentage points in mAP50 and mAP50:95, respectively. These results are obtained with 2.39 M parameters, 5.4 billion floating-point operations (GFLOPs), and an inference speed of 166 frames per second (FPS) on an NVIDIA RTX 4090 at 640×640 input resolution and batch size 1. Additional evaluations report the model’s behavior across clothing categories, target scales, overlap levels, image perturbations, hardware platforms, and external clothing datasets. Under the reported protocols, the framework improves detection accuracy with limited increases in parameters and computational cost.