In order to realize automatic tire defect detection and improve the accuracy of tire defect detection, a radial tire defect detection method (YOLOv7-DCA) was proposed. Firstly, the dilated coordinated attention-max pooling (DCA-MP) module was designed based on coordinated attention and void convolution, which strengthens the receptive field of subsampling while increasing the weight of defects and improving the defect detection accuracy. Secondly, the content aware reassembly of features (CARAFE) module was integrated in the Neck layer to further improve the defect detection accuracy. Experimental results show that the average detection accuracy of YOLOV7-DCA model can reach 97.77%, which is 3.27% higher than that of the original YOLOv7 algorithm. Compared with the current mainstream Faster-RCNN, YOLOv5, YOLOv7-tiny series models, the comprehensive performance is the best. It can be seen that this model provides a reference for the research of tire defect automatic detection.