Exposed distress is one of the common diseases of cement pavements, which seriously affects pavement service life and driving safety. Therefore, it is very important to detect and repair exposed distress in time. Traditional manual detection methods are low in both detection accuracy and detection efficiency. In view of this, our study proposes a method for detecting exposed defects of cement pavements based on an improved RetinaNet model. Firstly, preprocessing operations such as filtering and denoising are carried out on the exposed distress images collected by manual and inspection vehicles, and the model training data set is constructed. Then the SE Net structure is embedded into the feature extraction network, and the feature pyramid network is improved. Finally, the detection of exposed distress of cement pavements is realized by the improved RetinaNet. The results show that the improved RetinaNet model enhances the detection accuracy of exposed distress by 4.9% compared with the original model, which reaches 98.9%. In comparison with Faster R-CNN, SSD and YOLOv3 methods, the model significantly improved the detection effect for the same test data.