At present

the defects of quartz crucible mainly use manual eye inspection

which can not complete the accurate classification and full count only by manual. In this paper

a set of quartz crucible defect detection platform was built by using a 6-DOF manipulator and a rotating table

and high resolution images of crucible defects were obtained by combining backlight source and high speed camera. Simultaneously

the paper proposes an improved YOLOv5 algorithm for detecting crucible defects

capable of identifying various defect types such as impurity black spots

bubbles

and white spots. Firstly

K means clustering algorithm is used to generate anchor box suitable for crucible defect data sets

and then small target detection layer is added to improve the detection effect of small targets. Finally

omni-Dimensional dynamic convolution （ODConv） and the efficient channel attention （ECA） are used to make the network pay more attention to the targets to be detected without increasing too much computation. The experimental results show that in the self-built quartz crucible defect data set

the improved algorithm mAP@0.5 is 98.88% and the detection speed reaches 138 fps

which can meet the requirements of industrial detection.