An automatic machine of vision detection device for a workpiece with a complex curved surface, wherein the device is provided with at least one defect detection unit, the defect detection unit comprises a camera, light sources and an detection unit bracket; the camera and the light sources are arranged on the detection unit bracket; wherein one camera and a plurality of light sources configured for the camera are used as one detection unit, the distance between a light source and a to-be-inspected workpiece is five times greater than a shooting field of view of the camera; when the same detection unit performs a shooting task, the light sources are configured to be sequentially lightened; when a light source is lightened once, the camera is configured to shoot an image of the to-be-inspected object once; each time when the camera shoots, only one light source is configured to be lightened; and the strongest illumination areas of the light sources in the same detection unit on the to-be-inspected object are not overlapped with one another; the automatic machine of vision detection device further comprises a data processing system comprising a data processor, a graphic processor and a data memory, wherein the data processor is configured to read images from the data memory and input the images into the graphic processor; the data processing system is configured to reconstruct three-dimensional surface information of a to-be-inspected workpiece by using a plurality of images illuminated from different angles, characterized in that an image recognition network is preset in the graphic processor; the image recognition network comprises an image input unit, a feature extractor for acquiring effective features of the input images, a maximum pooling layer and a normal regression unit; wherein the image input unit is configured to input the images into an initial fusion module; the initial fusion module is configured to enable the images to be in one-to-one correspondence with illumination information of the images; the initial fusion module is configured to output the images fused with the illumination information to the feature extractor; the feature extractor comprises an inter-frame information extractor and a spatial information extractor; the inter-frame information extractor and the spatial information extractor are configured to use a three-dimensional convolution kernel, wherein the inter-frame information occupies one dimension, and the spatial information occupies two dimensions; an output of the initial fusion module is an input of the inter-frame information extractor, and an output of the inter-frame information extractor is an input of the spatial information extractor; and an output of the spatial information extractor is an input of the maximum pooling layer; the normal regression unit is configured to perform convolution operation on an output of the maximum pooling layer, and then to perform L2 regularization, wherein a result of the regularization is a normal map, and the normal map formed by normal vectors of all pixels represents the three-dimensional surface information of the to-be-inspected workpiece. the automatic machine of vision detection device further comprises a data processing system comprising a data processor, a graphic processor and a data memory, wherein the data processor is configured to read images from the data memory and input the images into the graphic processor; the data processing system is configured to reconstruct three-dimensional surface information of a to-be-inspected workpiece by using a plurality of images illuminated from different angles, characterized in that an image recognition network is preset in the graphic processor; the image recognition network comprises an image input unit, a feature extractor for acquiring effective features of the input images, a maximum pooling layer and a normal regression unit; wherein the image input unit is configured to input the images into an initial fusion module; the initial fusion module is configured to enable the images to be in one-to-one correspondence with illumination information of the images; the initial fusion module is configured to output the images fused with the illumination information to the feature extractor; the feature extractor comprises an inter-frame information extractor and a spatial information extractor; the inter-frame information extractor and the spatial information extractor are configured to use a three-dimensional convolution kernel, wherein the inter-frame information occupies one dimension, and the spatial information occupies two dimensions; an output of the initial fusion module is an input of the inter-frame information extractor, and an output of the inter-frame information extractor is an input of the spatial information extractor; and an output of the spatial information extractor is an input of the maximum pooling layer; the normal regression unit is configured to perform convolution operation on an output of the maximum pooling layer, and then to perform L2 regularization, wherein a result of the regularization is a normal map, and the normal map formed by normal vectors of all pixels represents the three-dimensional surface information of the to-be-inspected workpiece.