Damaged apple detection with a hybrid YOLOv3 algorithm

This paper proposes an improved You Only Look Once (YOLOv3) algorithm for automatically detecting damaged apples to promote the automation of the fruit processing industry. In the proposed method, a clustering method based on Rao-1 algorithm is introduced to optimize anchor box sizes. The clustering method uses the intersection over the union to form the objective function and the most representative anchor boxes are generated for normal and damaged apple detection. To verify the feasibility and effectiveness of the proposed method, real apple images collected from the Internet are employed. Compared with the generic YOLOv3 and Fast Region-based Convolutional Neural Network (Fast R-CNN) algorithms, the proposed method yields the highest mean average precision value for the test dataset. Therefore, it is practical to apply the proposed method for intelligent apple detection and classification tasks. (c) 2022 China Agricultural University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).