The invention relates to a
deep learning-based method and
system for inspecting embedding boxes before batch
dehydration. The method comprises the following steps of: acquiring
dehydration basket embedding box images and marking to construct a
data set; constructing an Eghost-YOLO model by adopting an EGhostC3 module and depth separable
convolution, and training models for respectively identifying an embedding box and a corresponding number by using a
data set; inputting the to-be-analyzed dehydrated
blueprint image into the embedding box detection model, and detecting and
cutting each embedding box; inputting the
cut embedding box image into a number detection model, and detecting and
cutting a number area; and identifying the
numbering area based on a pre-trained PP-OCRv4 character identification model, comparing the identified
numbering with a real
numbering, and outputting a
verification condition. Through a three-stage
cascade strategy, rotating frame detection positioning is carried out on an embedding box in the whole image;
cutting an embedding box area and accurately detecting a numbering area of the embedding box area; according to the method, omission, repetition and abnormal numbers are automatically marked, the number of the embedding box is automatically identified, omission and repetition problems are verified, full-process accurate tracking of
pathological samples is realized, and the reliability of
diagnostic data is ensured.