A defect detection method for Akidzuki pears based on computer vision and deep learning

Abstract

To quickly and accurately detect defects in Akidzuki (Pyrus pyrifolia Nakai) pears after harvest, this study aims to develop a method for Akidzuki pear defect detection based on computer vision and deep learning models. It mainly consists of obtaining high-quality images using an image acquisition system and proposing a new YOLO-AP detection model to identify defects in Akidzuki pears. The model uses YOLOv5 as the main architecture. The GhostDynamicConv (GDC) module is obtained by replacing the standard convolution in the Ghost module with a dynamic convolution. The C3-GhostDynamicConv (C3-GDC) module is obtained by replacing the Bottleneck module of C3 in Neck with the GDC module, simplifying the network while improving the model's accuracy. Meanwhile, Bottleneck Attention Module (BAM) is introduced after C3-GDC to refine the intermediate features. In addition, the original bounding box loss function is replaced with Wise-IoUv3 (WIoUv3) to accelerate the model convergence. The results demonstrate that YOLO-AP performs better in Akidzuki pear defect detection, with a mAP@0.5 of 0.939, a recall of 0.921, and a detection speed of 454.5 fps (2.2 ms per image). These values represent a 4.2 %, 3.5 %, and 1.4 % improvement over the baseline model. Comparing YOLO-AP with the updated YOLOv9 and other detection models, YOLO-AP is more accurate and faster. These findings demonstrated that the proposed method can detect Akidzuki pear defects in real time, efficiently and accurately, providing technical support for post-harvest defect detection.