# An Improved Lightweight Model for Defect Detection on Paths in Images
## 4 Discussion  
Road defect detection is an essential consideration in urban planning. Deep learning methods have been used by certain researchers [34,35] to address issues such poor multi-scale object perception, high missed detection rates for obstructed objects, and difficult model deployment. To facilitate the application of intelligent maintenance equipment by road maintenance personnel in urban roads, this study introduces the ACD-YOLOv8 model, which outperforms mainstream models in terms of lightweight performance. The ACD-YOLOv8 significantly reduces the computational complexity and quantity of parameters, providing more possibilities for its implementation on mobile or affordable embedded devices.  
Furthermore, the suggested ACD-YOLOv8 model shows notable improvements in accuracy, [EMAIL_ADDRESS], parameter count, and computational efficiency when compared to the original YOLOv8 model. However, to make the model lighter and use fewer parameters, although the model adopting the CCFM structure significantly reduces the parameter count and model size, by approximately 3.8 M and 7.7 MB respectively, performance in [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 has somewhat declined. The ADown module, which may assist in further refining the resolution of feature maps for more accurate target identification and improving the model's detection accuracy, is introduced in both the head and backbone parts of this study to solve this problem. The outcomes demonstrate that the aforementioned methods can effectively simplify the network structure, achieving the goal of lightweight feature fusion networks. It makes it possible for the model to do complicated jobs with exceptional precision and performance, enhances YOLOv8' detection capabilities for scale variations and small targets, and lowers the size of the trained model and the number of parameters by a significant amount. Meanwhile, this study adopts the DyHead head structure, aimed at optimizing the localization and classification functions in the target detection process. This structure integrates a variety of self-attention techniques, achieving sensitivity to different scales, spatial locations, and task requirements, thus promoting efficient learning of feature representation at different semantic levels, spatial areas, and task requirements.  
Despite the promising results, this study has several limitations that open avenues for future research. First, while the model performed well on the RDD2022 dataset, its generalization capability across datasets with different geographical locations, environmental conditions (e.g., adverse weather, poor illumination), and defect characteristics remains to be validated. Second, the performance on highly imbalanced or rare defect classes could be further improved. Therefore, future work should focus on three primary directions: (1) Enhancing the model's robustness and generalization by training on more diverse, multi-source datasets and employing advanced data augmentation techniques. (2) Investigating few-shot learning or transfer learning methods to improve detection performance for underrepresented defect categories. (3) Extending the current image-based detection framework to a video-based system to leverage temporal information for more consistent and reliable continuous road monitoring.