Screw sliding detection method based on modified Logistic regression and invariant moment
In order to detect the screw sliding and trip the measurement system is built and the related algorithms used in design of this system are researched. According to the reflection characteristic of the screw the appropriate light source is selected. The position and size of the screw are obtained by performing the Hough circle on the basis of image binarization. The center of the screw-circle is extracted and the Hu moment is calculated. Hu moments as characteristics are input to the improved Logistic regression classifier trained to detect whether the screw is slid鄄ing. The experimental results show that the screws with diameter of 5要8 mm under different ambient lights can be detected whether the screw is sliding. The accuracy rate can reach more than 95% which meets the require鄄ments of the screw sliding detection basically and the system is stable and reliable and has the certain anti-in鄄terference ability. When the learning rate σ is 0.01 this modified Logistic regression classification algorithm starts to converge after 33 steps while the traditional method takes 151 steps. When σ is 0.1 this method starts to converge after 35 steps but the traditional method is divergent due to the excessive learning rate.