The work aims to propose a blister area segmentation algorithm based on proportional features to quickly locate and segment the blister ROI, and detect defects in aluminum plastic blister medicine plates in combination with the image correlation feature algorithm, so as to solve the problems of slow localization and poor accuracy of ROI in images of aluminum plastic blister medicine plates. Firstly, original images of medicine plates in the packaging production line were collected through an industrial camera. Then, Blob analysis was used to separate the main part of the aluminum plastic blister from the original image. Then, the image was placed in the center area through affine changes and the blister area was segmented according to the proportional feature segmentation algorithm. Finally, defect detection was completed according to the pyramid accelerated NCC algorithm. The experimental results showed that the average NCC matching time of the image based on proportional feature segmentation was 9 ms. In the experiment with 20% defect samples, the false detection rate was 0.167% and the missed detection rate was 0.556%. By the segmenting precise blister ROI through proportional features and combining them with an improved NCC algorithm, the image matching time during defect detection is significantly reduced, which can effectively complete the defect detection task of aluminum plastic blister medicine plates.