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Abstract
During the process of steel casting, the task of worker is to identify the relative position of gantry hook and lifting lug of hot metal ladle so as to judge the appropriate time when gantry crane is to be lifted. Worker needs to stand near hot metal ladle, which does great damage to human’s body if exposed to such a bad environment. For this reason, the author has designed a machine vision system to displace artificial recognition. The machine vision system consists of the vision recognition algorithm based on AlexNet Convolution Neural Network (CNN) and the improved Canny edge detection algorithm. AlexNet CNN completes the preliminary recognition of the image and provides the samples that meet the recognition conditions to the improved Canny algorithm. The improved Canny algorithm completes the extraction of parameters such as the relative distance between the gantry hook and lifting lug and the tilt Angle of gantry hook. The sample images are extracted from two environments: model simulation and field simulation. Experimental data shows that the recognition algorithm based on AlexNet CNN can achieve 100% image recognition rate, which is higher than other traditional machine learning algorithms; the improved Canny edge detection algorithm can improve edge quality of extracting images in harsh environments.