The rapid detection of distracted driving behaviors is crucial for enhancing road safety and preventing traffic accidents. Compared with the traditional methods of distracted-driving-behavior detection, the YOLOv8 model has been proven to possess powerful capabilities, enabling it to perceive global information more swiftly. Currently, the successful application of GhostConv in edge computing and embedded systems further validates the advantages of lightweight design in real-time detection using large models. Effectively integrating lightweight strategies into YOLOv8 models and reducing their impact on model performance has become a focal point in the field of real-time distracted driving detection based on deep learning. Inspired by GhostConv, this paper presents an innovative GhostC2f design, aiming to integrate the idea of linear transformation to generate more feature maps without additional computation into YOLOv8 for real-time distracted-driving-detection tasks. The goal is to reduce model parameters and computational load. Additionally, enhancements have been made to the path aggregation network (PAN) to amplify multi-level feature fusion and contextual information propagation. Furthermore, simple attention mechanisms (SimAMs) are introduced to perform self-normalization on each feature map, emphasizing feature maps with valuable information and suppressing redundant information interference in complex backgrounds. Lastly, the nine distinct distracted driving types in the publicly available SFDDD dataset were expanded to 14 categories, and nighttime scenarios were introduced. The results indicate a 5.1% improvement in model accuracy, with model weight size and computational load reduced by 36.7% and 34.6%, respectively. During 30 real vehicle tests, the distracted-driving-detection accuracy reached 91.9% during daylight and 90.3% at night, affirming the exceptional performance of the proposed model in assisting distracted driving detection when driving and contributing to accident-risk reduction.

Drones for road crack detection provide a real-time Cyber-Physical System (CPS) solution, improving road surface assessment for efficient highway maintenance. CPS integrates computational algorithms with physical components, offering advantages over traditional manual inspections, which are slow and prone to false positives, especially in complex environments. To address these challenges, this paper presents the LEE-YOLO model, a novel and lightweight solution that leverages drones within a CPS framework to enable efficient road crack detection. The model incorporates a streamlined network built upon the YOLOv8n model and introduces an innovative lightweight fusion structure, SGC2f, which uses grouped convolutions and linear operations to reduce model weight and complexity markedly. Additionally, the proposed Efficient Bidirectional Feature Pyramid Network (EBiFPN) optimizes output channel utilization within the feature network, enhancing the model’s capacity to detect targets at multiple scales. Furthermore, the Efficient Multi-Scale Attention (EMA) module in the model’s backbone layer is designed to reduce redundant data and improve the extraction of relevant features. Experimental results demonstrate that the LEE-YOLO model outperforms YOLOv8n, achieving a 4.3% higher precision and a 4.0% improvement in mean average precision (mAP). Importantly, it achieves a 43.5% reduction in weight and a 39.5% decrease in computational demand, effectively balancing performance and efficiency. These advancements significantly enhance the potential for drone applications in highway maintenance and detection within the Cyber-Physical Systems domain.

SUMMARY: Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.