Research on Detection Algorithm of Tablet Surface Defect Based on Yolov3
DOI:
https://doi.org/10.54097/ajst.v1i1.241Keywords:
YOLOV3, Target detection, Tablet surface defect detectionAbstract
Tablet surface defect detection is an important part of tablet quality inspection, and manual detection and traditional pattern recognition methods are difficult to achieve the expected results. In this regard, this paper proposes a method for detecting tablet defects based on YOLOV3, which firstly uses industrial cameras to complete the collection of tablet defect images and creates a data set; then uses Daeknet-53 as the backbone network to initially extract features; secondly, constructs FPN feature pyramid enhancement Extraction of features; finally use yolo-head to obtain prediction results; detection mAP reaches 92.97% on the test set of the self-built data set. The experimental results show that the method has certain applicability and feasibility.
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