## 4 Framework Development and Validation
### Lane Assessment Algorithm Development  
The YOLO algorithm is widely used in computer vision due to its lightweight architecture and fast, accurate object detection. However, its focus on detection and classification limits its ability to assess object condition, importance, or risk--reducing its usefulness in decision-making contexts. To overcome this, the present study proposes an extended YOLO-based lane evaluation algorithm that incorporates quantitative assessment and explicit evaluation criteria.  
Figure 8: (**Left**) Confusion Matrix/(**Right**) Performance Results.  
Figure 9: (Left) comparison analysis results/(right) CNN confusion matrix.  
This algorithm builds on a previously developed lane identification model, adding a process in the head section to evaluate lane degradation. As shown in Figure 10, the algorithm consists of three key steps: applying image processing techniques, defining quantitative indicators to distinguish between intact and degraded markings, and classifying each lane as normal or worn.  
To develop the image processing techniques, it was necessary to extract individual lane images from the training data. In the original training images, both intact and worn lane markings were labeled together with the background, requiring separation from surrounding visual elements. To address this, approximately 1600 lane images containing background elements were extracted from the training dataset. The extraction process and resulting samples are presented in Figure 11.  
In the lane condition verification process, different image processing techniques are applied depending on the condition of the lane markings. For intact lanes, edge-filling is performed to ensure continuity without gaps, followed by the calculation of pixel occupancy. In contrast, for degraded lanes, edge extraction, secondary Canny edge detection, and morphological operations are used to remove noise and restore the lane area before computing the pixel occupancy ratio. Figure 12 provides a conceptual illustration of the lane condition evaluation process, and detailed algorithmic steps are outlined in Algorithm 2.  
Figure 11: Results of normal and faded lane extraction.  
Figure 10: Structure of the lane assessment algorithm.  
Figure 13 shows the analysis results for intact and worn lane markings. In intact lanes, few internal pixels were detected in the first Canny edge detection, indicating a good condition. In worn lanes, many internal pixels appeared initially but were removed through a second Canny detection and morphological operations, resulting in a low final pixel occupancy.  
Figure 13: Analysis results of normal and faded lanes.  
Figure 14 illustrates the pixel occupancy distribution for intact and worn lane markings, based on the lane evaluation algorithm shown in Figure 14. The analysis showed that intact lanes had pixel occupancy between 80.2% and 100.0%, while worn lanes ranged from 53.1% to 79.8%. Based on this, lanes with occupancy above 80% were classified as intact and those below 80% as worn. This threshold enables a quantitative assessment of lane degradation and supports consistent evaluation across various road environments. Although the effective data range starts from approximately 0.4, the figure retains the full 0.0-1.0 scale to ensure consistency and transparency in visual representation.