Abstract
Segmentation is the first and most important part in the development of any machine vision system with specific goals. Segmentation is especially important when the machine vision system works under environmental conditions, which means under natural light with natural backgrounds. In this case, segmentation will face many challenges, including the presence of various natural and artificial objects in the background and the lack of uniformity of light intensity in different parts of the camera's field of view. However, today, we must use different machine vision systems for outdoor use. For this reason, in this study, a segmentation algorithm was proposed for use in environmental conditions without the need for light control and the creation of artificial background using video processing with emphasizing the recognition of apple fruits on trees. Therefore, a video with more than 12 minutes duration containing more than 22,000 frames was studied under natural light and background conditions. Generally, in the proposed segmentation algorithm, five segmentation steps were used. These steps include: 1. Using a suitable color model; 2. Using the appropriate texture feature; 3. Using the intensity transformation method; 4. Using morphological operators; and 5. Using different color thresholds. The results showed that the segmentation algorithm had the total correct detection percentage of 99.013%. The highest sensitivity and specificity of segmentation algorithm were 99.224 and 99.458%, respectively. Finally, the results showed that the processor speed was about 0.825 seconds for segmentation of a frame.
Highlights
Performing segmentation operations in accordance with the desired purpose has different complexities
This complexity is because of crowded backgrounds with various objects. In applications such as site-specific spraying and combat weeds, segmentation is the first step in the design of machine vision systems [1,2,3,4]
Thethe most suitable color forinsegmentation is the color with minimum number of colors display of all thespace objects the image, because there space is the with a minimum number of colors and the display of all the objects in the image, because there is the the possibility of using threshold or thresholds with a very high accuracy
Summary
Performing segmentation operations in accordance with the desired purpose has different complexities. Objects in the images are divided into two classes of apple and background objects We show that 324 samples out of 42,750 apple samples are mistakenly located in the background objects class by the segmentation algorithm, so the segmentation algorithm has 0.758% error in this class. This algorithm mistakenly classified 691 samples of the objects in the background with the total members of 60125 in the apple class This leads to a 1.15% error in segmentation algorithm for this class. The percentage of total detection of the segmentation algorithm is 99.013% This accuracy is very good for this sample number, which proves the algorithm was configured properly. The accuracy is the percentage of total placement of the correct samples in their classes These three criteria are expressed using Equations 1 to 3.
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