TY - JOUR ID - 3805 TI - Providing Criterion to Automatic Evaluation of the Accuracy of Distribution of Tack Coat and Prime Coat Pavement Roads JO - Amirkabir Journal of Civil Engineering JA - CEEJ LA - en SN - 2588-297X AU - hajiali, mozhgan AU - Moghaddasnezhad, Fereydoon AU - ZAKERI, HAMZEH AD - Department of Civil Engineering and Environmental, Amirkabir University of Technology, Tehran, Iran AD - Department of Civil and Environmental Engineering, Amirkabir University of Technology(AUT), Thehran. Iran AD - RESEARCHER /AUT Y1 - 2021 PY - 2021 VL - 53 IS - 3 SP - 823 EP - 846 KW - Automatic system KW - Tack coat KW - Prime coat KW - Uniform distribution KW - Image processing DO - 10.22060/ceej.2020.16463.6250 N2 - The coating is one of the most important components that affect the efficiency of the pavements. Parameters are effective in the proper implementation of the coating such as the type of coating, the application time, the rate of application, temperature, uniformity of application, etc. The uniformity of application requires field control in the project implementation, is currently used to control the spreading weight of the tray. This test has many errors due to the lack of continuity. The issue of uniform distribution of coating has become less attention. In this study, the automatic system is presented based on image processing using a camera, GPS, microcontroller board, and ..., which can analyze the uniform distribution and provide a good, moderate and poor classification for coating distribution evaluation. Image quality has improved with image processing and compression and noise reduction have been done. The thresholding was used to separate the coating from the background. After the thresholding, various properties such as the area of the coating, coefficient of variation, local maximum, and minimum, etc. are obtained from the images and used to evaluate the coating distribution. Used categorization algorithms to select effective features in categorizing images. A comparison of the results of the classification of images by a confusion matrix. Finally, the results showed that the presented system has a precision of 86%. Also, using the effective parameters in the model, the uniform distribution index was presented. This index has a value between0 and 100, which indicates the best and worst distributions. UR - https://ceej.aut.ac.ir/article_3805.html L1 - https://ceej.aut.ac.ir/article_3805_0ab7e7bfc8486dae7b2993eb1e4aa5f4.pdf ER -