Method for identifying binary dot matrix code on mold surface

A method for identifying a binary dot matrix code on a mold surface, comprising:

• step S1, data acquisition:
• capturing a plurality of binary dot matrix code images of the mold surface by a camera, wherein a fiducial marker is arranged in each of four corners of each of the plurality of binary dot matrix code images, and a training set is constructed through annotating bounding boxes for each fiducial marker;
• step S2, model training:
• inputting data from the training set into a You Only Look Once version 8 (YOLOv8) network model to train the YOLOv8 network model to obtain optimal weight data of the YOLOv8 network model, and obtaining a trained and upgraded YOLOv8 network model by loading the optimal weight data into the YOLOv8 network model again;
• step S3, fiducial marker detection:
• capturing binary dot matrix code images of a mold surface to be tested by the camera to form a test set, inputting images of the test set to the trained and upgraded YOLOv8 network model, and outputting corner points of four bounding boxes of data of the test set by model training, each of the four bounding boxes is provided with 4 corner points, respectively, totaling 16 corner points;
• step S4, calculating peripheral vertices:
• filtering the 16 corner points by a filtering strategy, and calculating positional coordinates of vertices at an outermost periphery of the four bounding boxes;
• step S5, perspective correction:
• obtaining a distorted quadrilateral based on the positional coordinates of the vertices at the outermost periphery of the four bounding boxes, and converting the distorted quadrilateral into a standard L×L square pattern by perspective transformation;
• step S6, calculating a counterclockwise rotation angle:
• inputting the square pattern obtained in the step S5 into the trained and upgraded YOLOv8 network model of the step S2 for detection, and outputting four corner points of bounding boxes corresponding to a fiducial marker of the square pattern;
• obtaining a coordinate of a center point G of the bounding boxes based on the four corner points of the bounding boxes;
• comparing the coordinate of the center point G of the bounding boxes with a coordinate of a center point
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• of the square pattern obtained in the step S5 to obtain a counterclockwise rotation angle θ of the square pattern;
• step S7, rotational correction:
• rotating the square pattern of the step S5 counterclockwise by an angle θ by perspective transformation to align the square pattern to a correct position to obtain a standard pattern of binary dot matrix code images of the test set;
• step S8, code point identification:
• dividing the standard pattern into image blocks with a same size in equal portions based on a dimension of a binary code dots;
• and inputting the image blocks into the trained and upgraded YOLOv8 network model of the step S2 sequentially in the order of top to bottom and left to right for identification;
• in response to determining that a code point exists in the image blocks, outputting 1, and in response to determining that there is no code point in the image blocks, outputting 0;
• concatenating identification results to obtain binary-encoded sequence data;
• and
• step S9, decoding:
• decoding the binary-encoded sequence data based on binary-encoded decoding rules and deriving corresponding binary-encoded character information.