苹果采摘机器人视觉系统的暗通道先验去雾方法.

It is difficult to locate the apple in fog and haze environment for apple harvest robot. This paper proposed a new method to apply the principle of DCP (dark channel prior) to remove fog and haze on images which were collected from apple orchard. We adopted a new route to achieve the value of atmospheric light coefficient. Scan the hazed image with a 15×15 window, and get the smallest value of the 225 pixels from every window. All the smallest values constituted a dark channel image. The values of dark channel were stored in a matrix at the first step, and then the 1/1000 largest elements and their locations were calculated and stored in a new matrix which had the same shape with the dark channel matrix. Extract the matrix of red (R) channel of the hazed image at the next step. At the third step, the corresponding values in the matrix of R channel were obtained according to the position information in the new matrix. Finally, the average value of these values was calculated as the value of atmospheric light coefficient. According to the requirement of apple harvest robot, we took the haze-removal strength parameter of DCP algorithm as 1. In order to speed up the running of the algorithm, we calculated the transmission radio with guided filter. Image segmentation method used in the study had 3 stages: binaryzation, de-noising and dilation. Firstly, the grey image was obtained by calculating a special linear combination with red (R) channel, green (G) channel and blue (B) channel. This method emphasized the value of red channel in color images, which was conducive to separate the apple better at the next step. Secondly, binary image was obtained by Otsu method based on the grey image. Finally, after the process of de-noising and dilation, a better segmentation results could be obtained. We developed an experimental software with Microsoft Vision Studio 2010 and OpenCV (Open Source Computer Vision Library) to test the haze-removal effects in apple harvest robot vision system. The graphical user interface of the program was developed based on MFC (Microsoft Foundation Classes) library. The software had achieved the following functions: reading the image, calculating the dark channel value, calculating the value of atmospheric light coefficient based on the R channel, calculating the transmission radio, and so on. Based on this software we compared some haze-removal methods including MSR (multiscale retinex), AHE (adaptive histogram equalization) and DCP with different parameters. Hardware platform of the experiment was X230, which is a notebook computer produced by Lenovo Inc. We took Nikon D7100 camera as the image acquisition equipment and fixed it with tripod when it worked. Experimental images were collected at the apple base in Changping District of Beijing. Image acquisition dates were some days with heavy fog and haze in November 2015. Twelve images were selected as the experimental materials. After the analysis of the experimental data, this paper got the following results: 1) The average contrast value of the images was 64.04 with our method; the AHE method was faster, but the contrast value was 35.46 with the AHE; the histogram obtained by our method had the characteristics of Gaussian distribution, which showed that our method could get better image quality; 2) Testing the 640×480 images pixels, our method required 36.46 ms computing time, the MSR method required 126.43 ms, and the AHE required 28.58 ms. The time performance of our method was not as good as the AHE, but it was better than the MSR; 3) The average location accuracy was 94.8% with our method, which was higher than other methods. The experiments show that our method can get better balance between efficiency and performance. It is a feasible method for the actual apple harvest operation. [ABSTRACT FROM AUTHOR]