Neural Networks - Algorithms, Applications,and ... - Csbdu.in

112 BackpropagationAutomatic Paint QA System Concept. To automate the paint inspection process,a video system was easily substituted for the human visual system. However,we were then faced with the problem of trying to create a BPN to examineand score the paint quality given the video input. To accomplish the examination,we constructed the system illustrated in Figure 3.10. The input video imagewas run through a video frame-grabber to record a snapshot of the reflected laserimage. This snapshot contained an image 400-by-75 pixels in size, each pixelstored as one of 256 values representing its intensity. To keep the size of thenetwork needed to solve the problem manageable, we elected to take 10 sampleimages from the snapshot, each sample consisting of a 30-by-30-pixel squarecentered on a region of the image with the brightest intensity. This approachallowed us to reduce the input size of the BPN to 900 units (down from the30,000 units that would have been required to process the entire image). Thedesired output was to be a numerical score in the range of 1 through 20 (a1 represented the best possible paint finish; a 20 represented the worst). Toproduce that type of score, we constructed the BPN with one output unit—thatunit producing a linear output that was interpreted as the scaled paint score.Internally, 50 sigmoidal units were used on a single hidden layer. In addition,the input and hidden layers each contained threshold ([9]) units used to bias theunits on the hidden and output layers, respectively.Once the network was constructed (and trained), 10 sample images weretaken from the snapshot using two different sampling techniques. In the firsttest, the samples were selected randomly from the image (in the sense that theirposition on the beam image was random); in the second test, 10 sequentialsamples were taken, so as to ensure that the entire beam was examined. 4 Inboth cases, the input sample was propagated through the trained BPN, and thescore produced as output by the network was averaged across the 10 trials. Theaverage score, as well as the range of scores produced, were then provided tothe user for comparison and interpretation.Training the Paint QA Network. At the time of the development of this application,this network was significantly larger than any other network we had yettrained. Consider the size of the network used: 901 inputs, 51 hiddens, 1 output,producing a network with 45,101 connections, each modeled as a floating-pointnumber. Similarly, the unit output values were modeled as floating-point numbers,since each element in the input vector represented a pixel intensity value(scaled between 0 and 1), and the network output unit was linear.The number of training patterns with which we had to work was a functionof the number of control paint panels to which we had access (18), as well as ofthe number of sample images we needed from each panel to acquire a relativelycomplete training set (approximately 6600 images per panel). During training,4 Results of the tests were consistent with scores assessed for the same paint panels by the humanexperts, within a relatively minor error range, regardless of the sample-selection technique used.