BL-YOLOv8: An Improved Road Defect Detection Model Based on YOLOv8

Automated road damage identification remains a significant challenge for modern transportation infrastructure management. Prior research has shown that manual inspection techniques often suffer from high labor costs and slow processing speeds. No prior work had resolved the trade-off between detection accuracy and computational efficiency in real-time monitoring. That uncertainty drove the development of more sophisticated deep learning architectures for visual analysis. Existing models frequently struggle with the diverse scales and appearances of surface irregularities. This gap motivated the exploration of advanced feature extraction and attention mechanisms. Researchers have sought to optimize network structures to reduce hardware requirements for deployment. This study addresses these limitations by refining a standard object detection framework for specialized pavement applications.

The aim of this study is to develop an enhanced road defect detection algorithm to improve safety and maintenance efficiency. Researchers sought to address the limitations of traditional manual inspection methods, which are often costly and slow. The team focused on optimizing the YOLOv8s model to achieve a better balance between detection accuracy and computational requirements. This project was motivated by the need for faster, more reliable automated systems for infrastructure monitoring. The authors specifically aimed to reconstruct the neck structure to reduce the overall model size and parameter count. They also intended to improve operational speed by refining the feature pyramid layer. Furthermore, the study explored the use of dynamic attention mechanisms to enhance the model's ability to recognize complex surface damage. This work provides a systematic approach to upgrading deep learning models for specialized civil engineering applications.

The research team employed a comparative design to evaluate their modified deep learning framework against standard detection architectures. Review Approach framing focuses on the systematic reconstruction of the YOLOv8s backbone and neck. Investigators integrated the BiFPN concept to streamline the internal data flow. They implemented the SimSPPF module to optimize the feature pyramid layer for faster processing. The team introduced LSK-attention to enable dynamic large convolutional kernel operations across the network. This methodology allowed for a rigorous assessment of model accuracy and resource utilization. Researchers conducted experiments on pavement imagery to validate the effectiveness of these structural changes. They utilized standard metrics to quantify improvements in precision and computational efficiency relative to the original model.

Key Findings From the Literature indicate that the proposed model achieves a 3.3% improvement in average precision mAP@0.5. The researchers report a 29.92% reduction in total parameter volume compared to the baseline. Computational load decreased by 11.45% following the architectural refinements. These metrics confirm that the modified system maintains higher accuracy while requiring fewer hardware resources. The integration of the dynamic attention mechanism successfully expanded the receptive field for better object recognition. Experimental data demonstrate that the reconstruction of the neck structure effectively optimizes the model size. The results show that the system reliably identifies various forms of road damage. This evidence supports the utility of the proposed improvements for real-world detection tasks.

The authors demonstrate that their refined detection framework provides a robust solution for automated pavement monitoring. Synthesis and Implications suggest that the integration of dynamic attention mechanisms significantly enhances feature representation capabilities. The researchers propose that reducing parameter volume facilitates deployment on resource-constrained edge devices. Their findings indicate that architectural modifications effectively balance speed and precision requirements. The study confirms that the modified neck structure improves overall system performance compared to baseline configurations. These results offer a practical pathway for upgrading existing automated maintenance protocols. The authors emphasize that their approach provides a scalable reference for future infrastructure sensing technologies. This work validates the utility of targeted structural optimizations in deep learning for civil engineering tasks.