ICE-UNet: Lightweight Visual Segmentation of Aircraft Wing Leading-Edge Ice Accretion in Wind-Tunnel Images

Reliable visual delineation of ice accretion on aircraft wing leading edges remains challenging because accreted ice often exhibits low visual contrast, translucent and irregular morphology, and ambiguous boundaries. To address these challenges, this study proposes ICE-UNet, a lightweight semantic segmentation network for pixel-wise leading-edge icing detection. The network adopts a compact encoder–decoder architecture and integrates three functional modules: Residual Depthwise Separable Convolution with Batch Normalization and ReLU Activation (Res-DSCBR), Depthwise-Separable Receptive Field Blocks (DSCRFBs), and Convolutional Block Attention Modules (CBAMs). This design reduces model complexity while enhancing multi-scale contextual representation and attention-guided feature refinement. For empirical validation, a dataset of 5000 annotated image–mask pairs was constructed from 1300 original images comprising wind-tunnel video frames and post-icing photographs of the NACA 0020 and Boeing 117 airfoil models. Following augmentation, the 5000 pairs were randomly divided at the image level into training, validation, and test subsets in a 3:1:1 ratio. Experimental results show that ICE-UNet achieves 96.60% accuracy, 93.07% intersection over union (IoU), 96.36% precision, and 96.44% recall, with only 0.76 M parameters and 16.19 GFLOPs. Compared with the baseline U-Net, ICE-UNet improves IoU by 10.33 percentage points while reducing the parameter count and computational cost by 95.6% and 90.0%, respectively. Photometric perturbation tests further indicate that the model maintains stable segmentation performance under moderate brightness and contrast variations, although severe contrast degradation remains a limiting condition. TensorRT-based FP16 acceleration reduced the inference latency from 55.21 ms to 13.69 ms per image on a Tesla V100 GPU without measurable accuracy degradation. Additionally, a MobileNetV3 pre-screening module was integrated to effectively suppress false-positive activations on clean surfaces during continuous monitoring. Collectively, these findings show that ICE-UNet achieved the highest IoU among the ten segmentation models evaluated on this wind-tunnel dataset while retaining a compact architecture.