Attention-Driven Multi-Scale Clothing Detection Using an Enhanced SCS-YOLO Framework

Clothing detection is essential in computer vision applications like smart retail, virtual try-on, behavior analysis, and surveillance. However, challenges such as diverse clothing types, complex shapes, occlusions, and cluttered backgrounds hinder performance. To address these, the authors construct a diverse clothing detection dataset (DCDD) and propose the SCS-YOLO model, designed to improve multi-scale feature extraction and enhance key feature representation. The spatial depth convolution (SPD-Conv) module captures fine-grained details using spatial-to-depth and non-stride convolutions. The content-guided attention fusion (CGAF) module introduces channel and spatial attention for better robustness, while the squeeze and excitation attention (SEA) module adaptively weights critical features. Experiments on DCDD show that SCS-YOLO achieves 84.7% mAP, outperforming the baseline by 3.2%.