# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 4 Experimental Results  
When the CCD camera obtains an image, as the light spot is located somewhere in the image, the range of the light spot image center will be searched preliminarily, as shown in Figure 2. After the region of the light spot is found by the program, the center point of the light spot region is displaced to overlap the CCD image center, and the magnification of the CCD is increased to coincide with the size of the light spot region. The light spot region is thus separated, and the first search is completed [16; 17; 18]. This system uses a convolutional neural network to search for the laser spot region. The neural network is trained using images with multiple complete light spots and incomplete light spot modes so that the program can judge the position of the light spot in the image accurately and rapidly. Figure 2: Schematic diagram of laser spot positioning. When the light spot region is positioned in the image successfully, the first part will use the center of the light spot region as the image center. This deviates from the actual light spot center, and the system will consider that the positioned laser spot region may not be a complete light spot, as shown in Figure 3. The distance between the CCD image center and the light spot center is measured, and the two centers are shifted and overlapped. There are two processing modes, and whether the found light spot is complete or not is judged according to the aspect ratio of the light spot region. If it is a complete light spot, the centroid point of the light spot will be extracted using the centroid method, the distance between the CCD image center and the light spot center will be calculated, and the image center will be moved to overlap the light spot center. If it is not a complete light spot, the invariant moments will be calculated for the incomplete light spot region in the image and matched with the preset light spot source image to find out the position of the partial light spot region centroid in the light spot. The distance between the CCD image center and the light spot center is calculated, and the image center is moved to overlap the light spot center. The positioning effects of this system with different sample numbers are described below. A total of 25, 50, and 100 positive samples were used to train the neural network. Figure 4 shows the program results of using 25 positive samples and 50 negative samples to train the neural network. According to Figure 4a, the program could position the complete light spot successfully, but there was still misrecognition. The incomplete light spot in Figure 4b could not be recognized successfully. In order to improve the results, this system increased the sample number and the program was trained again. Figure 3: (**a**) Image of regional center that is not the light spot center; (**b**) incomplete laser spot image. Figure 4 shows the program results of using 50 positive samples and 100 negative samples to train the neural network. Figure 5a shows that the misrecognition of complete light spots was improved, but it was unable to position the light spot region only. Figure 5b shows there were many misrecognitions but the position of incomplete light spots could be recognized. In order to further increase the accuracy, the sample number was increased a second time and the network was trained again. After the neural network was trained using 100 positive samples and 200 negative samples, the program could position the region of complete light spots successfully. The misrecognition probability was reduced; however, it was still unable to position incomplete light spots. Table 1 shows the results of using three different sample numbers and changing the feature region size to train the neural network to recognize 100 target images. According to the data, the accuracy increased significantly with the sample number.