# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
## 5 Conclusions  
To enhance the precision of road damage detection and minimize the labor and material expenses associated with road damage assessment and maintenance, this paper proposes a road damage detection algorithm based on improved YOLOv8, named RDD-YOLO. This algorithm introduces several key optimization measures to improve model performance. To start with, the SimAM is introduced into the backbone network to enhance the focus on key information in the input image. This allows the model to selectively concentrate on critical areas in the input image, enabling more effective capture of relevant features related to road damage and significantly improving detection accuracy. Furthermore, an optimization is applied to the neck structure by replacing the traditional convolution module with GhostConv. This change achieves a more lightweight and efficient performance in the damage detection task while maintaining excellent performance in recognizing damage. Lastly, the upsampling algorithm of the neck is improved by replacing the nearest interpolation with bilinear interpolation, which better restores the subtle features of the image and improves the accuracy of the detection results through a finer interpolation method. Experimental results demonstrate that RDD-YOLO achieves an F1 score of 69.6% on the RDD2022 dataset, surpassing other algorithms not only in detection performance but also with more concise parameters. Although RDD-YOLO performs well, there is still room for improvement: the variety of road damage types, in reality, is extensive, while RDD2022 covers only four common types of road damage. Therefore, our model can only identify these four types of road damage. Additionally, in pursuit of higher F1 scores, we chose the large-scale YOLOv8x as the baseline model, which, despite improvements to reduce parameter count to some extent, remains quite large and may be challenging to deploy on resource-constrained, high real-time demand mobile endpoint devices. Moving forward, we will seek more comprehensive datasets to identify a greater variety of road damage types, further optimize network models, reduce model size and complexity, increase detection speed, and strive to further improve detection accuracy, enabling the model to be better applied to practical tasks such as road surface crack detection.  
Conceptualization, Y.L. ([PERSON]) and Y.L. ([PERSON]); methodology, Y.L. ([PERSON]) and Y.Y.; software, C.Y. and J.Z.; investigation, Y.L. ([PERSON]) and Y.Y.; data curation, J.Z.; writing--original draft preparation, C.Y.; writing--review and editing, C.Y.; visualization, Y.L. ([PERSON]) and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Science and Technology Innovation Action Plan Project: 22511100700.  
Figure 14: Sample images of road damage detection for each category.  
Data Availability Statement: The open-source dataset RDD2022 can be found at [[https://github.com/sekilab/RoadDamageDetector](https://github.com/sekilab/RoadDamageDetector)]([https://github.com/sekilab/RoadDamageDetector](https://github.com/sekilab/RoadDamageDetector)) (accessed on 1 March 2024).