In response to the current difficulties and high costs in segmenting radial tire defects in China, this paper proposes the following solution: a radial tire defect segmentation algorithm called Swin DAA based on Swin Transformer and attention feature pyramid. Swin Transformer is mainly used as the backbone feature extraction network, and the semantic expression ability is enhanced through the Dynamic Attenuation Attention feature pyramid, Build a software platform written in Python language, cascade the X-ray heavy-duty tire detection system to collect images, and use TCP protocol to communicate with the upper computer and transmit image data. Finally, connect the defect segmentation software system with the MES industrial control system to complete unmanned automated radial tire defect segmentation. The experimental comparison data shows that the Swin DAA network proposed in this article has an accuracy of 82.87%, a recall rate of 85.22%, and a transmission frame rate of 11 per second. The integrated software can effectively meet the actual monitoring requirements of radial tires.