Steel Bar Recognition And Counting Application Based On Android

In recent years, Chinese iron and steel industry has developed rapidly, steel bars as one of the necessities of the building are the main products of iron production. In China, counting and reviewing the bundle of steel bars mainly depends on manual counting. Although technologies required by Manual counting is not high, but it needs people’s high concentration for a long time. This will hurt the human’s eyes greatly, and Its accuracy is not high. Besides, the temperature of steel bars which were just burned is high, it will cause great harm to human body. All in all, the manual counting method is already unable to meet the actual requirements of the automation industry chain. In this paper, it presents the system of Steel Bar Recognition and Counting Application based on Android, which will count the packaged steel bar that are bundled.This article includes the following main parts:(1) Image acquisition and image pre-processing:Take the steel bar image collected in the field as the starting point of the scene, analyze and summarize the characteristics of the original images, study on image pre-processing methods, and design one image pre-processing algorithm to achieve good goals, this article will focus on image segmentation, image de-noising and binaring etc. operations.(2) Propose steel bar recognition and counting method:According to the steel bar image manipulated by image pre-processing, proposed steel bar recognition and counting based on adaptive radius template matching. The algorithm uses the ways of finding outline to look for steel section, and then uses the smallest enclosing circle to mark steel sections, and then use an iterative method to calculate the radius of the template, at last, count according to the radius of the template matching. Experiments show that the recognition accuracy of this method is high.(3) Systems design and test:Study and implement each functional module on the system, build the system platform, and take the optimization algorithm to improve the processing speed. Finally, taking steel bar images collected in the field as test-set, conduct several tests to verify the feasibility of the algorithm.