Bridge surface defect detection plays a critical role in ensuring traffic safety and facilitating infrastructure maintenance. A lightweight object detection network based on YOLOv10, termed LBSD-YOLO, is developed to achieve high detection accuracy while maintaining high efficiency for deployment on resource-constrained devices. The proposed framework consists of three main components: a feature extraction backbone, a feature fusion neck, and a detection head. In the backbone, the C2f\_FEMA (C2f with Feature Enhancement and Multi-branch Attention) module and the LAEDS (Lightweight Adaptive Encoder–Decoder for Sampling) spatial attention module are incorporated to enhance multi-scale feature representation.The neck incorporates multi-scale feature fusion with an Efficient Multi-scale Attention (EMA) mechanism. In the detection head, a lightweight DP-Head structure is developed, variant integrated with the DAMF\_CA coordinate attention for improved channel and spatial focus. Experiments are conducted on the self-built BDD-1234 dataset, which contains 6,617 high-resolution images covering six common bridge defect categories (cracks, spalling, exposed reinforcement, rust stains, efflorescence, and delamination). Compared to the baseline YOLOv10s, LBSD-YOLO reduces model size from 16.6 MB to 9.6 MB (42.2% reduction), computational complexity from 21.4 GFLOPs to 17.3 GFLOPs (19.2% reduction), and parameters from 7.2 M to 4.6 M (36.1% reduction), while achieving comparable detection performance (mAP@50 of 64.1% vs. 65.5%). The results demonstrate that LBSD-YOLO offers an efficient and accurate solution for real-time bridge defect detection on portable devices.

Fatigue driving is widely recognized as one of the major factors contributing to traffic accidents, posing not only a serious threat to road safety but also potential risks to drivers’ health and public security. With the rapid development of modern transportation, how to efficiently and accurately detect and warn against driver fatigue has become a critical issue in the field of intelligent transportation. To effectively address this issue, this paper proposes a novel fatigue driving detection method based on a Multi-Head Transformer with Adaptive Weighted Loss. In the proposed framework, the YOLOv8 model is first employed to efficiently and accurately locate key facial regions of the driver from real-time video streams, ensuring both high-speed processing and robustness. Subsequently, a Multi-Head Transformer model is introduced to capture the temporal dependencies and feature correlations among facial landmarks, enabling a more comprehensive characterization of fatigue-related behaviors such as eye closure, blinking, and yawning. In addition, an Adaptive Weighted Loss function is designed to dynamically balance the contributions of multiple fatigue features during training, effectively alleviating the class imbalance problem and enhancing the model’s generalization capability. Experimental results demonstrate that the proposed method maintains stable and superior detection accuracy even under complex long-duration driving conditions. Compared with traditional approaches, the system improves fatigue detection accuracy by 7.2%, achieving 95.5%, while also satisfying real-time requirements. In summary, this study presents an intelligent, adaptive, and efficient fatigue driving detection framework that provides a reliable theoretical foundation for intelligent warning systems and holds significant value for enhancing road traffic safety.

Pesticide residues enter a lake through the water cycle, causing harm to the water environment and human health. It is necessary to select highly sensitive fluorescence spectroscopy to detect pesticides (bifenthrin, prochloraz, and cyromazine), and a support vector machine (SVM) is used to analyze the concentration of pesticides. In addition, this paper adopts K-fold cross validation and a grid search to optimize the SVM algorithm. The performance evaluation index and running time prove the reliability of the results of this experiment. They show that fluorescence spectroscopy combined with SVM is efficient in predicting pesticide residue content.

The captan residues in apple juice were detected by fluorescence spectrometry, and the content level of captan was predicted based on a genetic algorithm and support vector machines (GA-SVMs). According to the captan concentration in apple juice, the experimental samples were divided into four levels, including no excess, slight excess, moderate excess, and severe excess. A GA was used to select the characteristic wavelength and optimize SVM parameters, and SVM was applied to train the classification model. 50 characteristic wavelength points were selected from the original fluorescence spectra, which contained 401 wavelength points, and the classification accuracy of the training set and test set is 99.02% and 100%, respectively, which is higher than the traditional PLS method. The results show that a GA can effectively select the feature wavelengths, and an SVM model can accurately predict the content level of captan residues. A fast and non-destructive analysis method, combined with a GA and SVM based on fluorescence spectroscopy, was realized.