Due to the numerous disadvantages that come with having anchors in the detection process, a lot of researchers have been concentrating on the design of object detectors that do not rely on anchors. In this work, we use anchor-free object detectors in the field of computer vision for surface defect detection. First, we constructed a surface defect detection dataset about real wind turbine blades, which was supplemented with several methods due to the lack of natural data. Next, we used a number of popular anchor-free detectors (CenterNet, FCOS, YOLOX-S, and YOLOV8-S) to detect surface defects in this blade dataset. After experimental comparison, YOLOV8-S demonstrated the best detection performance, with a high accuracy (79.55%) and a short detection speed (9.52 fps). All the upcoming experiments are predicated on it. Third, we examined how the attention mechanism added to various YOLOV8-S model positions affected the two datasets—our blade dataset and the NEU dataset—and discovered that the insertion methods on the two datasets are the same when focusing on comprehensive performance. Lastly, we carried out a significant amount of experimental comparisons.