<p>The accurate detection of pavement cracks is crucial for improving the safety of road surface as well as for the durability of infrastructure. Compared with conventional crack detection techniques, the proposed Yolov8-MED model more accurately detects pavement cracks. Recent advances have demonstrated the effectiveness of MHSA, ECA, and DCNv3 in computer vision tasks, motivating their use for feature extraction in deep learning–based crack detection. The challenge of integrating these techniques into the Yolov8-MED model while minimizing the computational load has emerged as a vital area of study in the field of automated pavement monitoring. As a result of these development, this study creates a Yolov8-MED design integrate MHSA, ECA and DCNv3 to enhance the model’s ability to capture fine crack details and improve detection accuracy. The aim is to enhance the crack location accuracy and keep the computational cost simultaneously. Further, to fine-tune the bounding box regression some modifications to the SCYLLA-IoU loss function have been made to improve on the localization of the bounding box. We also constructed the Xi’an Crack Dataset (XCD), which contains 7790 images of various crack types captured under diverse environmental conditions and is used to evaluate model performance. The Yolov8-MED model was benchmarked using the CrackSeg9k dataset and compared with the state-of-the-art models with a precision of 98.4%, an mIoU of 83.2%, and an F1-score of 90.8%, outperforming other models in key metrics. These results together with the mAP@0.5 of 90. 8% and mAP@0.5:0.95 of 70. 4% on the XCD dataset, corroborate this model’s outstanding performance for pavement crack detection and show how it may bring improvements to road maintenance tasks.</p>