Abstract
At present, the world's major pharmaceutical companies often identify defective tablets by visual inspection, but because of the fatigue of staff and the human eye's recognition limit of tiny parts, so there are often missed inspection, inefficient inspection phenomenon.<sup>[1]</sup> How to effectively monitor and ensure more qualified inspection rate in the inspection activities, which is the current problems encountered by pharmaceutical companies. How to efficiently detect and maintain a high detection rate in the testing process, which is currently the difficulties faced by pharmaceutical companies. In order to improve the service quality of the company, the adoption of machine inspection and sorting technology to replace manual inspection and sorting has become a trend. <sup>[2]</sup>Therefore, after systematic research, we obtained a method for monitoring surface defects of tablet products based on convolutional neural network. Using this method, we developed a tablet screening technique for surface defects, which significantly improved the accuracy and efficiency of surface defect detection.
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