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The electric shovel is a bucket-equipped mining excavator widely used in open-pit mining today. The prolonged direct impact of the bucket teeth with hard and abrasive materials such as ore during the process of the mining excavation can cause the bucket teeth to break and fall off prematurely, resulting in unplanned downtime and productivity losses. In response to this problem, we have developed a vision-based bucket teeth fault detection algorithm with deep learning. Using a dataset based on the images of both real shovel teeth and 3D-printed models, we trained a Faster Region Convolutional Neural Network (Faster R-CNN) to obtain the number of normal bucket teeth and the positions of the bucket teeth from the images, using the additional bucket dataset from 3D-printed models to pre-train the network for improving its detection accuracy on the real bucket data. We compared the resulting Faster R-CNN model with the ZFNet, the ResNet-50, and the VGG16 and found our Faster R-CNN model to perform best in terms of accuracy and speed.