# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 4 Result Analysis
### Data Processing and Training Configuration  
RDD 2022 contains 47,420 images, of which 36,000 have annotations from six distinct countries. Approximately 14,700 of the 36,000 annotation files were found to be empty as they did not contain an object, so those images were manually removed by us. YOLO is designed to detect objects in a single pass across the entire image; unlike two-stage detectors (such as Faster R-CNN), this method does not require negative photos for training. The dataset had a country-specific sub-folder for each country's images and corresponding annotations. China-Drone was omitted in our work because it needed to be obtained from a front-mounted vehicle image for this research. A close inspection of the RDD 2022 dataset provides us with a few insights for selecting images efficiently. Countries such as Norway and Japan had various image sizes. For example, the resolution of the Norway photos (3072 \(\times\) 3720) is incompatible with our GPU configuration. Each image was manually scaled to 640 \(\times\) 640 to train the YOLOv5 model. Numerous photos have distinct labels for a single country. For instance, China Motorbike has a label named Repair. Japan and India have multiple labels with the uncommon names D01, D11, D43, D44, and D50. In light of this, the unique classes were manually eliminated, the remaining classes were merged, and the four common categories were retained for six countries. Four types of damage were included: D00: longitudinal cracks, D10: transverse fractures, D20: alligator cracks, and D40: potholes. Figure 6 depicts an example of these four classes.  
The dataset was split 70:20:10 for training, validation, and testing purposes. After all preprocessing is completed, the model is evaluated using 1542 images. The testing size is sufficient for estimating the model's performance on new, unobserved data. This model was trained with 10,798 images and validated with 3042 images to prevent overfitting. The results of the different classes of this study and their quantity are presented in Figure 7, which includes details about the dataset's class and amount.  
Upon creation of the training set, the YOLOv5s model is subsequently trained through the utilization of the yolov5s.pt pretraining weights, and a \"custom.yaml\" was created as the configuration file to specify the model architecture. Logarithmic and weight files are recorded and stored throughout the training process. The training configuration is given in Table 1. For this research, the Google Colab Pro platform was utilized, operating in a Linux environment and offering substantial computational resources. These included an Intel Xeon CPU with a clock speed of 2.2 GHz, 32 GB of RAM, and Nvidia A100 GPU. These resources played a pivotal role in meeting the demanding computational requirements of deep learning experiments and model training.  
Figure 5: Damage category distribution in the current benchmark state-of-the-art dataset.  
Figure 6: Different kinds of damage used in this research. (**a**) D00: longitudinal crack; (**b**) D10: transverse crack; (**c**) D20: alligator crack; (**d**) D40: pothole.