Bruise Detection Using Faster R-CNN

In this study, it is aimed to classify of the apples as bruised and robust by using Faster R-CNN which is one of the convolutional neural network methods on images taken from apple fruit. In the proposed model, the process steps are the image acquisition-preprocessing, the determination of the caries regions, and the classification of the apples. During the image acquisition-preprocessing phase, a NIR camera is used, which is located within a designed image acquisition platform. In the study, a total of 1200 images were obtained from 6 different angles of each of a total of 200 apples, 100 of which were bruised and 100 of which were robust. In the pre-processing phase, adaptive histogram equalization, edge detection, morphological operations are applied to these images, respectively. Caries were identified with the Faster R-CNN model trained using new images with improved visibility by applying preprocessing. In classification phase, 84.95% correct classification rate has been reached in the detection of bruised and robust apples. As a result, it is thought that the proposed model can be used for automatic detection of bruised and robust apples in juice food industry