Pavement disease detection is particularly important in road maintenance, and the YOLOX-GED algorithm is proposed for the problems of complex background of pavement image and large difference of disease scale in pavement disease detection. On the basis of YOLOX-s algorithm, the algorithm firstly designs CSP_Ghost module to replace CSPLayer module, which reduces the number of network parameters and at the same time strengthens the feature extraction ability of the network; secondly, introduces the ECA attention mechanism, which strengthens the feature fusion effect of the network, and improves the recognition accuracy of the network on the pavement lesions; lastly, designs the pyramid structure of DSPPF space, which increases the diversity of features and strengthens the recognition accuracy of the network on the pavement lesions. that increases the diversity of features and strengthens the extraction and fusion of multi-scale contextual information. Experiments on the RDD2020 dataset show that the mAP of the YOLOX-GED algorithm is 5.32% higher than that of the YOLOX-s algorithm, and at the same time, the amount of model parameters is reduced by 7.9%, which makes it easier to deploy to mobile devices.