A ceramic substrate is an important basic material for semiconductor components. Detecting defects in it is of great significance for ensuring high product quality. An automatic defect detection method for a ceramic substrate based on the improved YOLOV4 network was proposed in this paper. To ease the difficulty associated with defect detection caused by small defect size， varying color and shape， and large size variation between different kinds of defects in a ceramic substrate， the improved YOLOV4 model optimized the design of the initial prior box by referring to the Complete Intersection over Union （CIoU） idea. The model then introduced the Confidence Loss function based on the Gradient Harmonizing Mechanism （GHM） and CRISS-Cross Attention Net （CCNet） to improve the defect detection ability. The experimental results show that the average accuracy of the detection method based on the improved YOLOV4 model for ceramic substrate defects， including stain， foreign matter， gold edge bulge， ceramic gap and damage， can reach 98.3%. This accuracy meets the industry requirements for the detection accuracy of ceramic substrate defects.