A defect detection method for aluminum-plastic blister capsule packaging based on GoogLeNet network model is proposed in order to address the poor detection effect brought on by the color, size, and picture noise of aluminum-plastic blister capsules. To locate the Plate batch number region to be detected, the normalized product correlation gray scale matching method is first used. Next, the drug plate′s capsule blister region is divided by the improved gray value projection method, and the dataset for aluminum-plastic blister capsules is created. Finally, the improved GoogLeNet network model is trained and tested to realize the defect recognition of the missing grain, concave cap, and other defects. The experimental findings demonstrate that the improved horizontal-vertical projection method achieves 100% segmentation accuracy for capsule blister area, and the recall rate of network for defect recognition is over 98.64%. The improved gray value projection technique offers excellent segmentation capabilities and strong robustness. The improved network, which can be used for the quality inspection of aluminum-plastic blister tablet packaging, has considerably increased the accuracy of fault identification of aluminum-plastic blister capsule pharmaceutical board packaging when compared to previous methods.