Abstract: Aiming at the problem of slow recognition and inaccurate localization of small mechanical parts based on machine vision, this paper proposes a method of recognition and localization of small mechanical parts by combining Improve U-Net (IU-Net) and minimum bounding rectangle（IU-Net-MBR）. Firstly, a visual sorting test platform is built to produce a data set of small mechanical parts.Secondly, in order to improve the feature extraction efficiency, the feature extraction network of U-Net is replaced by a lightweight MobilenetV2 network, which reduces the parameters of the model and the amount of computation.Then, in order to improve the segmentation accuracy and the robustness of the U-Net, the SE (squeeze and excitation) attention module.Finally, the length and width basic parameters of the parts are obtained using the minimum outer connection matrix to realize the part identification and localization. The experiments show that IU-Net improves 4.39% and 3.82% in mean intersection over union (Miou) and pixel accuracy (PA) relative to U-Net. In processing images, the speed of IU-Net is improved by 76.92% relative to U-Net. compared to mainstream segmentation models, IU-Net achieves better segmentation results and effectively improves the segmentation accuracy of small mechanical parts. In the grasping test, IU-Net-MBR achieves 100% and 96.67% in recognition rate and grasping rate, respectively.