In this research, 500 high resolution rubber bearing images with damages on them are collected and manually labeled to build the data set. Then the data set is adopted to train a Fully Convolutional Network (FCN) model, aiming to predict damages on the rubber bearings from a large amount of high-resolution images. The method is called Cropping Segmentation, which uses cropped image with size 224×224 as input images to train the FCN model, instead of traditional Squashing Segmentation. However, even though Cropping Segmentation has high accuracy, there are a lot of noises in the background. To solve the problem, Context Detection is carried out to exclude noises in the background. By intersecting the outcomes of Cropping Segmentation and Context Detection, the predicted damages on the rubber bearing are retained, while the noises outside the bearing are removed. Context Detection includes CNN-based Context Classification and FCN-based Context Segmentation. By testing and comparing, Context Segmentation has a better performance on finding rubber bearings pixels.