A tire X-ray images defect detection algorithm based on CSPDarknet53 backbone network and PANet (path aggregation network) neck network called TireX DefectNet algorithm was proposed, which could improve the detection accuracy of tire X images caused by factors such as low grayscale values in photos, low contrast between targets and backgrounds, narrow defect shapes, and small defect targets. Firstly, in order to reduce the loss of features during network transmission, deformable convolution and cross layer feature fusion modules (CLEF) were introduced in the backbone network. Secondly, the ASPPF (atrous spatial pyramid pooling with folding) module was constructed and add to the backbone network, which was based on the spatial pyramid pooling module, and could improve the feature extraction ability of the network for narrow targets. Finally, the CASA (channel attention and spatial attention) module was constructed to make the model more focused on detecting small target defects, thereby further improving detection accuracy. The experimental results show that the improved TireX-DefectNet algorithm has improved accuracy by 8.8% compared to its baseline network, and provides an effective method for tire X-ray images defect detection.