Road surface crack detection based on improved YOLOv5s.

In response to the issues of low efficiency and high cost in traditional manual methods for road surface crack detection, an improved YOLOv5s (you only look once version 5 small) algorithm was proposed. Based on this improvement, a road surface crack object recognition model was established using YOLOv5s. First, based on the Res2Net (a new multi-scale backbone architecture) network, an improved multi-scale Res2-C3 (a new multi-scale backbone architecture of C3) module was suggested to enhance feature extraction performance. Second, the feature fusion network and backbone of YOLOv5 were merged with the GAM (global attention mechanism) attention mechanism, reducing information dispersion and enhancing the interaction of global dimensions features. We incorporated dynamic snake convolution into the feature fusion network section to enhance the model's ability to handle irregular shapes and deformation problems. Experimental results showed that the final revision of the model dramatically increased both the detection speed and the accuracy of road surface identification. The mean average precision (mAP) reached 93.9%, with an average precision improvement of 12.6% compared to the YOLOv5s model. The frames per second (FPS) value was 49.97. The difficulties of low accuracy and slow speed in road surface fracture identification were effectively addressed by the modified model, demonstrating that the enhanced model achieved relatively high accuracy while maintaining inference speed.

CFM-YOLOv5:CFPNet moudle and muti-target prediction head incorporating YOLOv5 for metal surface defect detection.

Aiming at the problem of low efficiency of manual detection in the field of metal surface defect detection, a deep learning defect detection method based on improved YOLOv5 algorithm is proposed. Firstly, in the feature enhancement part, we replace the multi-head self-attention module of the standard transformer encoder with the EVC module to improve the feature extraction ability. Second, in the prediction part, adding a small target detection head can reduce the negative impact of drastic object scale changes and improve the accuracy and stability of detection. Finally, the performance of the algorithm is verified by ablation experiments and analogy experiments. The experimental results show that the improved algorithm has greatly improved mAP and FPS on the data set, and can quickly and accurately identify the types of metal surface defects, which has reference significance for practical industrial applications.

Research on oil boom performance based on Smoothed Particle Hydrodynamics method.

To address the issues of fluid-solid coupling, instability in the liquid two-phase flow, poor computational efficiency, treating the free surface as a slip wall, and neglecting the movement of oil booms in simulating oil spill containment, this study adopts the Smoothed Particle Hydrodynamics (SPH) method to establish a numerical model for solid-liquid coupling and liquid two-phase flow, specifically designed for oil boom containment and control. The DualSPHysics solver is employed for numerical simulations, incorporating optimized SPH techniques and eight different skirt configurations of the oil boom into the numerical model of two-phase liquid interaction. By setting relevant parameters in the SPH code to enhance computational efficiency, the variations in centroid, undulation, and stability of undulation velocity for different oil boom shapes are observed. The experimental results demonstrate that the improved oil boom exhibits superior oil containment performance. These findings provide a theoretical basis for the design of oil boom skirt structures.