Abstract: YOLO standing for You Only Look Once is one of the most famous algorithms in computer vision used for detecting objects in a real-time environment. The newest version of this algorithm, namely YOLO with the seventh version or YOLOv7, is proposed in the present study for cashew nut detection (good, broken and not peeled) in packaging and quality inspection lines. Furthermore, this algorithm using an efficient convolutional neural network (CNN) to be able to successfully detect and identify unsatisfactory cashew nuts, such as chipped or burnt cashews. In order to deal with the quality inspection process, a new dataset called CASHEW dataset has been built at first by collecting cashew images in environments with different brightness and camera angles to ensure the model's effectiveness. The quality inspection of cashew nuts is tested with a huge number of YOLOv7 models and their effectiveness will also be evaluated. The experimental results show that all models are able to obtain high accuracy. Among them, the YOLOv7-tiny model employs the least number of parameters, i.e. 6.2M but has many output parameters with higher accuracy than that of some other YOLO models. As a result, the proposed approach should clearly be one of the most feasible solutions for the cashew’s quality inspection.

Van-Hung Pham, Ngoc-Khoat Nguyen and Van-Minh Pham, “A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131243

@article{Pham2022,
title = {A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131243},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131243},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Van-Hung Pham and Ngoc-Khoat Nguyen and Van-Minh Pham}
}