| dc.description.abstract | Many high-precise mechanical parts and electronic components need to be precisely inspected based on 3D information. However, non-contact visual inspection has some problems of incomplete 3D data. If the incomplete data are applied for defect detection or 3D model reconstruction, there would result in detection loss or false construction; thus, the depth image completion becomes an important issue currently.
To the issue of depth image completion for electronic components on printed circuit boards, we will develop a supervised deep learning system. Deep learning is simulating the structure and operation mechanism of human neurons. Based on the training of a large amount of data, the convolutional neural network (CNN) can show off excellent ability and give an expected inference result according to the input data.
The goal of this studying is using a CNN to achieve two different-meaning application tasks: depth image completion and translation. Depth image completion has a better repair quality, but needs more kinds of training data; flawed depth images and the related perfect depth images are necessary; if we want to obtain better results, flawed color images are needed. The repair quality from translation is limited, but only flawed color image and the perfect depth image are needed in training. In general, a deep learning system requires a huge amount of training data and the data need consuming large manpower to collect. If the completion or translation mode can be alternatively selected based on the available data at hand, it will reduce cost and enhance the flexibility of usage. The number and contents of the input images for the two different-meaning tasks are different, to simplify the input structure of the proposed CNN, we also need to input a full-black image for the unnecessary image.
The proposed CNN is modified from the lightweight RefineNet. The main modifications include: (1) using the encoding/decoding architecture; (2) using EfficientNet as the backbone of encoder; using lightweight RefineNet modules and pre-activation residual block as the decoder; (3) using deconvolution operations in up-sampling to replace interpolation in decoder; (4) adjusting the complexity between encoder and decoder, and adding an adaptive residual spatial attention module to the decoder for further improving the ability of completion.
In this experiment, we used images of electronic components on printed circuit board as training data, including flawed depth images, perfect depth images, and flawed color images. The proposed CNN can effectively repair missing parts; comparing with the lightweight RefineNet, the average absolute error of the depth image is reduced by 35 %. For completion and translation tasks, the mean absolute errors (MAE) of the completion and translation modes are 1.39 and 3.04 grayscale values, respectively. | en_US |