Enhanced YOLO using Attention for Apple grading

The average age of agricultural workers is getting older caused of the declining birthrate and aging population. Though the labor shortage is becoming more serious, much labor is needed to check defects such as scratches and rot on apples before shipping. This research proposes a method to automatically inspect the appearance of deliverables by a deep learning-based object detection method. In detail, we use YOLO, which is used widely as an object detection model, deployed the attention module, changed the resolution of feature maps, and optimized the times of convolution. Our research aims to make higher accuracy and fewer parameter models. The model’s parameter is also improved from 5.05G to 0.73 G. Our proposal methods improve the detection score from 86.04% to 87.59%. The experimental results show the proposal helps automate the visual inspection of apples and alleviate labor shortages in the agricultural industry.