The application of deep learning in crack detection has become a research hotspot in Structural Health Monitoring (SHM). However, the potential of detection models is often limited due to the lack of large-scale training data, and this issue is particularly prominent in the crack detection of ancient wooden buildings in China. To address this challenge, “CrackModel”, an innovative dataset construction model, is proposed in this paper. This model is capable of extracting and storing crack information from hundreds of images of wooden structures with cracks and synthesizing the data with images of intact structures to generate high-fidelity data for training detection algorithms. To evaluate the effectiveness of synthetic data, systematic experiments were conducted using YOLO-based detection models on both synthetic images and real data. The results demonstrate that synthetic images can effectively simulate real data, providing potential data support for subsequent crack detection tasks. Additionally, these findings validate the efficacy of CrackModel in generating synthetic data. CrackModel, supported by limited baseline data, is capable of constructing crack datasets across various scenarios and simulating future damage, showcasing its broad application potential in the field of structural engineering.

In response to the decreased accuracy in person detection caused by densely populated areas and mutual occlusions in public spaces, a human head-detection approach is employed to assist in detecting individuals. To address key issues in dense scenes—such as poor feature extraction, rough label assignment, and inefficient pooling—we improved the YOLOv7 network in three aspects: adding attention mechanisms, enhancing the receptive field, and applying multi-scale feature fusion. First, a large amount of surveillance video data from crowded public spaces was collected to compile a head-detection dataset. Then, based on YOLOv7, the network was optimized as follows: (1) a CBAM attention module was added to the neck section; (2) a Gaussian receptive field-based label-assignment strategy was implemented at the junction between the original feature-fusion module and the detection head; (3) the SPPFCSPC module was used to replace the multi-space pyramid pooling. By seamlessly uniting CBAM, RFLAGauss, and SPPFCSPC, we establish a novel collaborative optimization framework. Finally, experimental comparisons revealed that the improved model’s accuracy increased from 92.4% to 94.4%; recall improved from 90.5% to 93.9%; and inference speed increased from 87.2 frames per second to 94.2 frames per second. Compared with single-stage object-detection models such as YOLOv7 and YOLOv8, the model demonstrated superior accuracy and inference speed. Its inference speed also significantly outperforms that of Faster R-CNN, Mask R-CNN, DINOv2, and RT-DETRv2, markedly enhancing both small-object (head) detection performance and efficiency.

Construction safety texts are commonly used only for descriptive statistical analysis, and systematic approaches for uncovering latent semantic risk correlations remain limited. In particular, risk identification and prioritization under unlabeled conditions remain challenging. To address this issue, this study proposes a semantic risk association and ranking framework based on Sentence-BERT (SBERT). First, a domain-specific keyword library is constructed, and representative risk terms are extracted through tokenization, stop-word removal, and TF-IDF weighting. A fine-tuned SBERT model is then employed to generate sentence embeddings. FAISS-based similarity search is applied to match safety inspection records with historical accident reports, enabling automatic identification and ranking of the most relevant accident types. In addition, a seven-day inspection window is introduced to capture the temporal accumulation effect of hazards and support risk assessment without explicit labels. Experiments conducted on 1368 accident reports and 484 inspection records show that the proposed framework achieves an accuracy of 0.75, a recall of 1.00, and an F1-score of 0.8571. Cross-project validation yields an F1-score of 0.5607, and the performance remains stable under 10% noise interference. The results demonstrate that the proposed semantic risk association and ranking framework is effective and robust for practical construction safety management.

Aiming to address the problems of scale uncertainty, feature extraction difficulty, model training difficulty, poor real-time performance, and sample imbalance in low-resolution images for gender recognition, this study proposes an efficient pedestrian gender recognition model based on key area feature extraction and fusion. First, a discrete cosine transform (DCT)-based local super-resolution preprocessing algorithm is developed for facial image gender recognition. Then, a key area feature extraction and information fusion model is designed, using additional appearance features to assist in gender recognition and improve accuracy. The proposed model preprocesses images using the DCT image fusion and super-resolution methods, dividing pedestrian images into three regions: face, hair, and lower body (legs) regions. Features are separately extracted from each of the three image regions. Finally, a multi-region local gender recognition classifier is designed and trained, employing decision-level information fusion. The results of the three local classifiers are fused using a Bayesian computation-based fusion strategy to obtain the final recognition result of a pedestrian’s gender. This study uses surveillance video data to create a dataset for experimental comparison. Experimental results demonstrate the superiority of the proposed approach. The facial model (DCT-PFSR-CNN) achieved the best accuracy of 89% and an F1-Score of 0.88. Furthermore, the complete pedestrian model (MPGRM) attained an mAP of 0.85 and an AUC of 0.86, surpassing the strongest baseline (HDFL) by 2.4% in mAP and 2.3% in AUC. These results confirm the high application potential of the proposed method for gender recognition in real-world surveillance scenarios.

To address challenges including severe occlusion, large-scale variation, inaccurate keypoint localization, and strict real-time constraints in subway surveillance scenarios, this paper proposes a lightweight human pose estimation framework based on an enhanced YOLOv8-Pose architecture. The proposed method introduces four key components: a DWR (Dilation-wise Residual) module to expand multi-scale receptive fields for improved joint context modeling, an HFERB (High-Frequency Enhancement Residual Block) to recover fine-grained structural details lost during downsampling, an OrthoNets-based orthogonal attention mechanism to reduce channel redundancy while preserving discriminative representations, and an MFEB (Multi-Scale Feature Enhancement Block) to enable adaptive feature fusion under varying crowd densities. Extensive experiments on the COCO 2017 dataset demonstrate that the proposed method achieves 87.9% mAP@0.5 and 64.9% mAP@0.5–0.95, improving the baseline YOLOv8-Pose by 1.7% and 1.0%, respectively, while reducing validation pose loss by 5.77%. After lightweight optimization with ONNX export and FP16 inference, the model is compressed to 3.99M parameters and achieves 76 FPS inference speed on the same hardware, significantly improving efficiency compared with the baseline, while achieving 38 FPS on the full model. Both configurations satisfy real-time constraints for subway surveillance deployment. Qualitative results on real subway surveillance videos further demonstrate strong robustness under occlusion and scale variation. Overall, the proposed method achieves a favorable trade-off between accuracy and efficiency, making it suitable for real-time human pose estimation in intelligent transportation systems.