Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special...Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special circle center target board is designed to calibrate the camera,and then the registration of the homography matrix is optimized by using the improved RANSAC(Random sample consensus)algorithm combined with the designed special target board,and the parameters of the wheel alignment system are adjusted by using the space vector principle.Accurate measurements are made to obtain the parameters of the four-wheel alignment.Design a calibration comparison experiment between the traditional target board and the new type of target board,and conduct a comparative test with the existing four-wheel aligner of the depot.The experimental results show that the use of the new target board-binding optimization algorithm can improve the calibration efficiency by about 9%to 21%,while improving the calibration accuracy by about 10.6%to 17.8%.And through the real vehicle test,it is verified that the use of the new target combined with the optimization algorithm can ensure the accuracy and reliability of the four-wheel positioning.This method has a certain significance in the rapid detection of vehicle four-wheel alignment parameters.展开更多
Aiming at the stability of the circuit board image in the acquisition process,this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and R...Aiming at the stability of the circuit board image in the acquisition process,this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm.The device detection model and data set are established based on Faster RCNN.Finally,the number of training was continuously optimized,and when the loss function of Faster RCNN converged,the identification result of the device was obtained.展开更多
基金Anhui Province Key Research and Development Program(No.2022107020012)Shenzhen Science and Technology Innovation Project(No.JSGG20191129102008260)。
文摘Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special circle center target board is designed to calibrate the camera,and then the registration of the homography matrix is optimized by using the improved RANSAC(Random sample consensus)algorithm combined with the designed special target board,and the parameters of the wheel alignment system are adjusted by using the space vector principle.Accurate measurements are made to obtain the parameters of the four-wheel alignment.Design a calibration comparison experiment between the traditional target board and the new type of target board,and conduct a comparative test with the existing four-wheel aligner of the depot.The experimental results show that the use of the new target board-binding optimization algorithm can improve the calibration efficiency by about 9%to 21%,while improving the calibration accuracy by about 10.6%to 17.8%.And through the real vehicle test,it is verified that the use of the new target combined with the optimization algorithm can ensure the accuracy and reliability of the four-wheel positioning.This method has a certain significance in the rapid detection of vehicle four-wheel alignment parameters.
文摘Aiming at the stability of the circuit board image in the acquisition process,this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm.The device detection model and data set are established based on Faster RCNN.Finally,the number of training was continuously optimized,and when the loss function of Faster RCNN converged,the identification result of the device was obtained.
