Aiming at the problems of low accuracy of small target detection, large number of parameters, as well as misdetection and leakage in tile surface defect detection, an improved tile surface defect detection algorithm, YOLOv9s-SEFN, is proposed. Firstly, the SPNet multi-scale feature fusion module is designed in this study to effectively improve the model′s detection of small defects on the tile surface by enhancing the network′s capability of capturing and fusion of multi-scale feature expression; second, the ECG lightweight fusion module is designed to reduce the computational and parametric quantities to achieve lightweighting; then, the frequency adaptive dilation convolution (FADC) is introduced to improve the accuracy of small defects detection on tiles by adaptively adjusting the dilation rate and frequency selection; and lastly, a new loss function, NWD-EIOU, is designed to improve the accuracy of small target localization by combining EIOU and NWD. The experimental results show that compared with the original YOLOv9s detection algorithm, the improved YOLOv9s-SEFN algorithm performs better on the self-built experimental dataset, with the mAP@0.5 raised to 93.2%, an improvement of 3.5%; the recall rate is raised by 4.96%; the amount of parameters is reduced by 2.3%; and the amount of floating-point arithmetic is reduced by 4.0%, which is able to satisfy the needs of tile surface defect detection.