In vision-based load swing angle detection of bridge cranes,the fast load movement leads to motion blur in the images captured by the camera,which reduces the detection accuracy.To obtain the load swing angle accurately and in real time,an improved detection method of load swing angle based on kernelized correlation filter is proposed.The trajectory prediction is introduced into the load tracking process to avoid the load escaping the search window due to its rapid motion.A scale adaptive strategy is designed to improve the tracking performance.The adaptive learning rate is updated according to the load velocity and the peak response gradient to accommodate the feature changes of the goal.The image of the region of interest is processed by greyscale enhancement to reduce the effect of motion blur on detection accuracy.The center and radius of the mark are obtained according to the triangle outer circle theorem,and the measurement model of the load swing angle in consideration of the crane working space is established.The experimental results show that,the proposed method is superior to other methods in terms of detection accuracy and real-time performance.The detection error is not more than 3 pixel,and the processing speed is not lower than 35 frame per second.
万方数据
维普
