# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 6 Results and Discussion  
### Results  
During the operation and use phase, the quality of a road is essential not only for ensuring efficient transportation and the smooth flow of goods but also for the safety of all traffic participants/road users [40; 41]. Even small signs of damage, no matter how minor, can potentially lead to serious accidents. Detecting these damages quickly is vital as it ensures both the safety of traffic participants and helps in the more effective allocation of maintenance and repair budgets. The most significant outcome of this research is the successful development of a software called RTI-IMS, which can autonomously and accurately detect road damages promptly. Scientific research in the field of information technology always demands freshness and staying up-to-date with trends. This is a crucial requirement for researchers [42; 43]. However, the basis for comparing research results often relies on the evaluations of previous studies. To demonstrate the excellence of the software developed in this research, this study established optimal evaluation criteria, primarily focusing on model detection performance/accuracy and model detection speed. As a result, alongside the successful development of a model building on the achievements of previous scientific research, this study also showcases the superior advantages of the model and its capability to meet the needs of transportation infrastructure managers. To demonstrate the superiority of the model in the road infrastructure sector compared to previous research, the two most important evaluation criteria for a machine learning detection model, accuracy and processing speed, have been proposed to compare RTI-IMS with previous road damage detection models, showcasing superior results. In terms of improving the average precision (AP) value, all detection models using the RTI-IMS detector achieved a higher mean average precision (mAP) value compared to models using other detectors, specifically achieving an mAP of 80.19% at an Intersection over Union (IoU) of 0.5%. RTI-IMS was developed based on the Yolo V5 software with SSD detection, achieving superior mAP results as shown in Table 2, Figure 8.  
The AP (average precision) values of comparative models are related to the mean average precision (mAP) values of machine learning detection models. These values represent the performance accurately, and they were used to compare the results of the RTI-IMS model with existing models in previous research. Typically, Fast-CNN (F-CNN) is used to develop models for Fast CNN-based machine learning detection to demonstrate superiority (50-65%). However, with the triple fusion method applied in this research, the RTI-IMS developed on the Yolo V5 (2023) platform showed the opposite results. Table 3 shows a mAP value of 74.52%, which demonstrates its superiority compared to average values of around 50%. For each type of road damage, the research also calculated detailed performance results of the models for each type of damage (IoU = 0.5). The mAP values show that Faster R-CNN Inception Resnet V2 is the most effective model for detecting three out of five types of damage (I, II, and III) with respective AP values of 71.64%, 45.28%, and 70.77%. Faster R-CNN Inception V2 is the best model for detecting type V damage (blurry) with an AP value of 76.59%. However, with the upgraded 2023 version of YOLO V5 applied in the RTI-IMS, the software demonstrates superiority in all mAP values, with values of 79.76%, 75.23%, 75.85%, 70.65%, and 77.04% for each respective type of damage (Table 4), making it suitable for effectively detecting all types of road damage. With IoU machine learning recognition performance indicators (0.5) and the average being \(\geq\) 80%, it has proved to have relatively high image detection performance (Figures 9 and 10). These images are used to compare the performance of an RTI-IMS model containing full instances of each object detection class on the same conditions.