Surface defect detection has received increased attention in relation to the product quality and industry safety. This paper develops an image pyramid convolution neural network (IPCNN) model to detect surface defects in images. The IPCNN is an improvement of the Mask rcnn model. It combines image pyramid and deep convolution neural network to extract pyramid features for defect detections. First, it constructs an image pyramid. Then, images from the pyramid are processed with a deep residual neural network to extract features. Extracted features are then fused and processed with a feature pyramid network to generate pyramid features. A region proposal network then operates on the pyramid features to generate defect bounding boxes and make classifications. Finally, a fully convolutional neural network generates defect masks in the detected bounding boxes. The IPCNN was evaluated on the publicly available COCO dataset and improved the Mask rcnn by approximately one point. Additionally, it generated high precision and recall values on inspecting the oil leak defect on freight train with a small dataset. The comparison with state-of-the-art methods on freight train defect detection showed the promising results of IPCNN. The efficiency of the IPCNN is 0.243 seconds per frame.
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