In production of bottled liquor, there are usually surface defects on the bottle cap that affect the quality of the product. The work aims to propose an improved algorithm model DTS-YOLO based on YOLOv5s to solve problems of low detection efficiency of blemishes on the surface of liquor bottle caps and poor detection of small targets. First, deformable convolution was introduced into the backbone network to improve the detection accuracy of the model for extreme aspect ratio defects; Secondly, the Transformer coding block was incorporated into the backbone network to make the backbone network focus on the extraction of global information of the image; Finally, influenced by Inspired by the C3 module in YOLOv5s, the C3SE-Lite module was designed. The C3 module was embedded in the SE attention module and the GhostConv convolution was introduced at the same time, so that the model could reduce the number of parameters while enhancing the ability to detect defects. The experimental results showed that under the premise of reducing the number of parameters by 10%, the average precision of the method in this paper reached 95%, and the average detection speed was 30 f/s. The method presented in this paper can effectively detect the surface defects of bottle caps quickly and accurately, and can be widely applied to the surface detection of bottle caps during the production of bottled liquor.