The defect of the PDC bit compact is an important factor affecting the drilling efficiency, and detecting whether the PDC bit compact is defective is a prerequisite for repairing the PDC bit. In order to reduce the false detection of PDC drill bit composites and improve the detection accuracy, a target detection algorithm based on improved YOLOv5 is proposed. This method is based on the YOLOv5 network, and integrates the RepVGG reparameterization module to enhance the feature extraction ability of the network; introduces the coordinate attention mechanism in the C3 module, embeds the position information in the channel attention mechanism, and improves the target detection ability of the defective composite film. Improve the bounding box regression loss function to the WIoU loss function, and formulate a suitable gradient gain allocation strategy. The experimental results show that the precision rate of the improved network increased with 2%, the recall rate increased with 0. 9%, and the mean average precision ( mAP) increased with 1. 3%, reaching 98%, which can realize the defect recognition of PDC drill bit composites.