# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Deep Learning Approaches
#### 4.3.3 Semantic Segmentation  
For quantitative distress assessment, convolutional neural networks perform pixel-level segmentation and area calculation after region selection. Unlike object detection, pixel segmentation precisely delineates distress contours. Current algorithms primarily target challenging-to-identify road cracks, achieving over 90% pixel accuracy (PA). Although the parameter counts vary among different network models, pixel segmentation generally achieves high-precision recognition at the cost of model complexity and training dataset preparation difficulty. For instance, YOLO series models have around 4M parameters, while pixel segmentation models reach approximately 30M [64]. [PERSON] et al. [65; 66; 67] pioneered the application of U-Net and SegNet in pavement crack detection. The U-Net architecture, originally designed for biomedical image segmentation [68], leverages skip connections to preserve fine-grained spatial details (Figure 8)--a critical advantage for capturing crack morphology. In contrast, SegNet [69] employs pooling indices during upsampling to reduce computational overhead, albeit at the cost of segmentation precision in low-contrast scenarios (e.g., faint asphalt cracks). Subsequent studies have enhanced pixel segmentation networks through several technical improvements: incorporating lightweight separable convolution blocks [70], integrating attention mechanism modules [71; 72; 73; 74; 75; 76; 77], and reducing network parameters [78; 79] to optimize crack segmentation performance and computational efficiency. [PERSON] et al. [80] proposed EfficientCrackNet, a lightweight hybrid architecture integrating a CNN with Transformer modules. The model reduces computational complexity from 15.42 FLOPs (G) to 0.483 FLOPs (G) and decreases parameters from 14.72 M to 0.26 M compared to conventional DeepCrack [81]. Parallely, [PERSON] et al. developed a novel deep generative adversarial network named \"CrackGAN\" [82], which utilizes partially accurate synthetic labeled samples for training, effectively reducing the workload involved in preparing training data for the model. Figure 8: Skip connections in U-Net. The pixel-level semantic segmentation of road cracks faces two major challenges: (1) severe class imbalance due to sparse crack pixels, and (2) complex crack morphologies with varying detection difficulties. These factors significantly hinder segmentation performance. To address these challenges, our previous work developed a CNN-based semantic segmentation model [83; 84] specifically designed for crack detection on concrete surfaces. We introduced a dual pre-modification deep learning strategy that simultaneously addresses class imbalance through adaptive weighting and reduces the influence of easily classifiable samples via a difficulty balancing factor. This approach yields the modified binary cross-entropy loss function as expressed in Equation (1):  
\[\text{CE}= \begin{cases}-w_{1}(1-p)^{\gamma}\log(p),\ y=1\\ -w_{2}p^{\gamma}\log(1-p),\ y=0\end{cases} . \tag{1}\]  
The formulation defines \(y\) as the ground truth label (where 1 indicates cracks and 0 represents background), \(p\) as the predicted probability of \"\(y\) = 1\", \(\gamma\) as the sample difficulty balancing factor (optimized to 2 through empirical testing), and \(w\) as the adaptive weight whose calculation process is detailed in Equation (2):  
\[w_{i}=\frac{1/C}{a_{i}/n}=\frac{n}{a_{i}\cdot C}. \tag{2}\]  
Here, \(i\) indexes the class category (with \(C\) = 2 in our binary classification task), \(n\) denotes the total pixel count in the dataset, and \(a_{i}\) represents the pixel count for class \(i\). The proposed model ultimately achieved 99.79% pixel accuracy (PA) in concrete surface micro-crack detection, with detailed segmentation results of test samples presented in Figure 9.  
Figure 9: Optimization of model recognition performance [83].