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Image semantic segmentation has great development in many fields, and the lack of fully supervised segmentation labels has always been a major problem in the development of image semantic segmentation. In this paper, we propose a WAILS method to solve this problem. First, the image is coarsely segmented through a weakly supervised network at the image level. Second, to further obtain the shape of the target in the image, the watershed algorithm is used to refine the result of the coarse segmentation. Third, this refined image is used as a label for the first round of training of a fully supervised image semantic segmentation network. At last, the results are refined through the watershed as the label of the second round of fully supervised training and then it iterates. This method achieves the pixel-level semantic segmentation only through image-level labels and watershed pre-segmentation. Our method achieved good performance on the PASCAL 2012 dataset and the COCO dataset, while its segmentation accuracy surpasses all current weakly supervised semantic segmentation models in the category of bird, airplane, sheep, and so on.INDEX TERMS Semantic segmentation, weakly supervised, watershed algorithm.

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Based on the requirement of small errors in measuring roundness (cylindricity) error, a leveling methodology which using a dual-point vertical layout has been put forward and analyzed. According to the direction cosine of the axis of workpiece, the amount of leveling has been defined and calculated, which overcomes the problem brought by manual adjustment technology and forms theoretical bases of fast, accurate leveling and high precise measurement. The assessment of roundness (cylindricity) error is to search for a center (cylinder axis) which satisfies the minimum condition. Due to the reliance of initial solutions and relative slowness in terms of convergence precision and convergence rate when using Nelder-Mead simplex method, a combinative method of Quasi-Newton and N-M simplex method has been proposed which achieves a fast, accurate search for global optimums. With the proof of the simulation of classical testing functions using Matlab and the measured data, the convergence rate and precision will be enhanced effectively has been certified with the combination of both methods mentioned above which ensuring and improving the measuring precision of workpiece.

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