To address the challenges of road crack appearance characteristics being susceptible to environmental interference, high miss detection rate of fine cracks, and limited computational resources of inspection equipment, a lightweight detection model, MCA-YOLO-A, is proposed. The model is based on YOLOv8n, replacing the original backbone with a lighter MobileNetV3 feature extraction network, and integrating a CA attention module that accurately captures spatial information, thereby enhancing the capability of feature extraction. Meanwhile, the Alpha-IOU loss function suitable for lightweight networks is introduced, which makes the overall performance of the network improved. In addition, a small target detection layer is added to improve the recognition accuracy of fine cracks. The average precision of MCA-YOLO-A model on road crack data sets is 0.930 and 0.893, respectively, which is 7.0% and 9.7% higher than that of the original YOLOv8n model, and the parameter quantity is only 6.0M, which is 4.8% lower, and the detection speed reaches 95 frames/s. The experimental results demonstrate that the model is highly accurate, lightweight, and capable of generalization, which makes it more suitable for deployment in scenarios with limited computational resources such as embedded systems and mobile devices.