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In view of the current field of image vision, there are few studies on the defective screws in transmission lines, and in the traditional image processing methods, the recognition accuracy of screws is not high. This article adopts a method of identifying defective screws based on contextual semantic segmentation information. On the basis of Deeplab v3 + network, image cropping and blocking and adaptive Gamma correction enhanced preprocessing are performed on the transmission line data set to mIoU for identifying defective screws Increased by about 17%; for misidentification of common screws, a method of combining context semantic segmentation information is proposed, and the area of the missing screw is segmented and several surrounding component areas are analyzed for topological relationship, and the misidentified common screws are excluded according to the type of topological relationship . The results of multiple experiments show that the method of identifying defective screws using preprocessing and combining contextual semantic information is superior to Deeplab v3 + algorithm.