The traditional quality detection method for transparent Nonel tubes relies on human vision, which is
inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would
lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the
uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar
method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection
accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed
based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is
presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping
relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient
explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external
diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the
defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance
can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human
resources and ensure the quality of Nonel tubes.