Accurate pavement crack segmentation is essential for intelligent transportation systems and infrastructure maintenance. However, due to the low contrast, complex background interference, and elongated structural characteristics of cracks, existing segmentation methods often suffer from discontinuous predictions and missed detection of tiny crack regions. In particular, CNN-based methods are limited in capturing long-range dependencies, while Transformer-based methods tend to lose fine-grained spatial details during patch tokenization. To address these challenges, we propose a DINOv3-based crack segmentation framework termed CrackDINO. Specifically, we design an RGB-guided Multi-scale Feature Pyramid (RGMFP) module to enhance hierarchical semantic interaction across different feature resolutions. In addition, a Crack-aware Stable Attention (CAS) module is introduced to strengthen weak crack responses and improve discriminative representation for thin and low-contrast crack regions. Furthermore, a Cascaded Hierarchical Multi-scale Decoder (CHMD) is proposed to progressively recover spatial details and preserve crack continuity during feature reconstruction. Extensive experiments on the Crack500 and CrackForest Dataset indicate that the proposed method achieves competitive performance compared with several representative segmentation models, including U-Net, DeepLabV3+, SegFormer, TransUNet, and Swin-Unet. On the Crack500 Dataset, CrackDINO achieves an mIoU of 62.61\% and an F1-score of 74.16\%. On the CrackForest Dataset, the proposed method obtains an mIoU of 60.08\% and an F1-score of 74.79\%, demonstrating favorable robustness and generalization performance for fine-grained crack segmentation.
Jin-Lai Zhang· Poster Volume 0007 The 2026...· 0 citations
Time series forecasting is fundamental to intelligent decision-making systems, enabling proactive planning and resource optimization across diverse application domains. However, the inherent complexity of real-world time series—including multi-scale temporal patterns, heterogeneous variable dependencies, and dynamic non-stationarity—poses significant challenges for existing forecasting models. Current approaches often suffer from high-frequency information attenuation in frequency-domain modeling, inadequate characterization of scale heterogeneity across variables, and limited capability to capture time-varying dynamics. To address these challenges, this paper introduces AdaDyTS, a unified knowledge-driven forecasting framework that synergistically integrates three complementary mechanisms: multi-scale frequency-domain interpolation decoupling via the Cascaded Spectral Residual Extractor (CSRE), dynamic morphological perception via the Dynamic Morphological Perception Unit (DMP-U), and time-variant state-space inference via the Time-Variant State-Space Module (TV-SS). CSRE separates low-frequency trends from high-frequency residuals through coarse-to-fine layer-wise self-reconstruction, preserving transient information that static filters typically attenuate. DMP-U employs deformable convolution guided by multi-expert attention to adaptively adjust receptive fields, enabling fine-grained modeling of local fluctuations and nonlinear distortions. TV-SS relaxes the conventional time-invariant parameter assumption, dynamically modulating state transition parameters to capture both short-term variations and long-term dependencies. Under a unified evaluation protocol across 13 benchmark datasets, AdaDyTS achieves average improvements of 4.35\% in MSE and 4.31\% in MAE over the AMD backbone, consistently outperforming state-of-the-art methods across long-horizon forecasting scenarios. The proposed framework demonstrates the effectiveness of integrating domain-specific knowledge—including spectral analysis, morphological feature extraction, and dynamic system modeling—within a unified deep learning architecture for enhanced predictive performance.
Jin-Lai Zhang· Poster Volume 0008 The 2026...· 0 citations
Traffic sign recognition is a safety-critical perception task in intelligent transportation systems, requiring accurate classification under complex real-world conditions including illumination variation, viewpoint changes, motion blur, and environmental degradation. Existing methods often rely on single-branch attention mechanisms that capture only partial feature dependencies, limiting robustness under degraded visual conditions. To address these limitations, we propose MCSCA, a Multi-dimensional Collaborative Spatial-Channel Attention network that integrates three complementary attention branches—Neuron Saliency Enhancement (NSE), Spatial-Channel Collaborative Calibration (SCC), and Cross-Dimensional Interaction (CDI)—through a learnable Softmax-weighted adaptive fusion strategy. The three branches operate in parallel on shared intermediate feature maps, simultaneously enhancing neuron-level saliency, spatial-channel contextual dependency, and cross-dimensional structural interaction. The fused representation is further stabilized via residual connection. The proposed model is built upon a lightweight residual backbone with multi-scale feature aggregation and is trained using AdamW with warmup-cosine scheduling, CutMix/Mixup augmentation, and label smoothing. Experiments on GTSRB demonstrate that MCSCA achieves 99.89\% validation accuracy, 99.97\% precision, and 99.80\% recall at 633.4 FPS with only 4.38M parameters, maintaining competitive performance while preserving real-time inference efficiency. Robustness evaluation on GTSRB-C, a corrupted benchmark covering 8 camera corruption types at 5 severity levels, shows a mean corruption accuracy (mCA) of 81.81\% and a composite RobScore of 76.91, with near-perfect robustness under photometric corruptions (Fog mCA: 99.85\%, Rain mCA: 98.87\%) and graceful degradation under additive noise and motion blur. These results validate the effectiveness of the proposed multi-branch collaborative attention design for robust traffic sign recognition under real-world perturbations.
Jinlai Zhang· Poster Volume 0007 The 2026...· 0 citations
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