# CL-YOLOv8: Crack Detection Algorithm for Fair-Faced Walls Based on Deep Learning
## 4 Experiments
### Contrast Experiment  
To comprehensively validate the detection performance of the improved YOLOv8 algorithm proposed in this paper, we conducted comparisons with current state-of-the-art models for wall crack detection under the same configuration environment, using identical validation and training datasets. We selected Precision, Recall, [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, Parameters, and GFLOPs (giga floating-point operations per second) as our evaluation metrics. The improved algorithm was compared against the five following models: YOLOv8,  
Figure 8: Comparison of F1-confidence curves before and after improvement.  
Figure 7: Comparison of P–R figures before and after improvement.  
According to Table 1, YOLOv8n exhibited higher detection precision, recall rate, and mAP values compared to the Faster R-CNN, YOLOv7-tiny, YOLOv8s, and YOLOv8l models. Although YOLOv5s slightly surpasseed YOLOv8n in some metrics, the parameters and total floating-point operations of YOLOv5s were approximately twice those of YOLOv8n. Moreover, YOLOv8n had fewer parameters and GFLOPs compared to the other seven networks, making it the chosen base model for improvement. The proposed CL-YOLOv8 algorithm, while slightly lower in accuracy compared to YOLOv5s and YOLOv8s, had fewer parameters and computational demands than these models. Additionally, its recall rate and mAP values exceeded those of YOLOv5s and YOLOv8s. CL-YOLOv8 outperformed in terms of recall rate and mAP values as well. Despite doubling the parameter count and total floating-point operations compared to the original YOLOv8n, the improved model showed enhanced precision, recall rate, and mAP values, with a 3.9% increase in mAP compared to the original YOLOv8n algorithm.  
At the same time, this study compared the ConvNeXt V2 model with other advanced feature extraction networks and evaluated the LSKA attention mechanism against other attention modules. The results demonstrated that both ConvNeXt V2 and the LSKA module exhibited a superior detection accuracy and performance. The results are shown in Tables 2 and 3.  
While it had slightly higher parameters and GFLOPs, this enhanced accuracy suggests that ConvNeXt V2's advanced architecture, which likely incorporates larger convolutional kernels and deeper layers, effectively improved feature extraction capabilities without compromising speed too much.  
The YOLOv8n+LSKA configuration achieved an [EMAIL_ADDRESS] of 83.4%, outperforming models using the SE, GAM, CBAM, and Biformer attention mechanisms. This indicates that the LSKA mechanism significantly enhanced the model's ability to capture and utilize contextual information across larger regions, leading to a better detection accuracy, particularly in complex environments. The balance between model complexity (parameters and GFLOPs) and performance also highlights the efficiency of the LSKA mechanism in improving feature extraction and fusion.  
These results underscore the effectiveness of integrating ConvNeXtV2 and LSKA into the YOLOv8n framework, offering a more robust solution for tasks requiring a high detection accuracy, such as crack detection in challenging environments.