Automatic Detection of Oranges Peel Based on the YOLOv5 Model

Abstract

In recent years, the deep learning algorithms have been widely used in target detection with the development of artificial intelligence algorithms. This experiment tends to detect whether the oranges are totally peeled or not by using the YOLOv5 automatic detection system. By using this automatic detection, the factory can classify the peeled and unpeeled orange easily and successfully, hence improving the efficiency and quality of the production of orange products. To achieve this goal, this experiment uses the YOLOv5s.pt model for training the detection; for collecting the images, considering the real situation, this experiment collect the whole orange with peel, partly peeled, the whole orange without peel, and some cloves of peeled oranges; overall 1100 images were collected, 310 came from the real-life photos and other found online; the ratio of the number of training images and the validation images is 8:2; set up two classes which are “orange with peel” and “peeled orange”; Using the online tool to label the images for training; Using the PyTorch (1.12.1) as the deep learning library. The experimental result shows that the proposed method achieved 88.4% for the Mean Average Precision (mAP) value and 88.7% for the average recall rate. Besides, in the testing part, this result can successfully detect the “orange with peel” and the “peeled orange”.