The current real-time semantic segmentation algorithm has the high computational cost and large memory footprint， which cannot meet the applications requirements of actual scenes. In order to solve the problems， a new type of shallow lightweight real-time semantic segmentation algorithm — AEFNet （Real-time segmentation algorithm based on Attention mechanism and Effective Factorized convolution） was proposed. Firstly， one-dimensional non-bottleneck structure （Non-bottleneck-1D） was adopted to construct a lightweight factorized convolution module to extract rich contextual information and reduce the amount of calculation. At the same time， the learning ability of the algorithm was enhanced in a simple way and the extraction of detailed information was facilitated. Then， the pooling operation and Attention Refinement Module （ARM） were combined to construct a global context attention module to capture global information and refine each stage of the algorithm to optimize the segmentation effect. The algorithm was verified on the public datasets cityscapes and camvid， and the precision of 74.0% and the inference speed of 118.9 Frames Per Second （FPS） were obtained on the cityscapes test set. Compared with Depth-wise Asymmetric Bottleneck Network （DABNet）， the proposed algorithm has the precision increased by about 4 percentage points， and the inference speed increased by 14.7 FPS. Compared with the recent efficient Enhanced Asymmetric Convolution Network （EACNet）， the proposed algorithm has the precision slightly lower by 0.2 percentage points， but has the inference speed increased by 6.9 FPS. Experimental results show that the proposed algorithm can more accurately identify the scene information， and can meet the real-time requirements.

In order to improve the accuracy of identification and localization of cell microorganisms by optical micropositioning system, on the one hand, the hand-eye calibration method should be optimized, on the other hand, the accuracy of global image recognition should be improved. Aiming at those, a two-step method for hand-eye calibration of the system was proposed. Firstly, the origin of the system was determined by calibrating the fixed target, and the transformation relationship of the vision module to the origin of the system was obtained. Then, according to the starting point position of each photograph, the number of photoing and the step size of movement, the transformation relationship of the global image to the origin of the system was solved. Finally, in order to further improve the accuracy of the global transformation relationship, an error correction method based on Fourier transform was used to obtain the error of the visual module in movement,then the error was added into the system for compensation. Experimental results show that after error compensation, the micropositioning system has the error mean value in X-axis direction reduced from 10.23 μm to -0.002 μm, the error mean value in Y-axis direction reduced from 6.9 μm to -0.50 μm, and the average positioning accuracy over 99%. The results show that the proposed method can be applied to the optical micropositioning system for high-precision automated capture of cell microorganisms.