Apple Surface Defect Detection Based on Improved YOLOv5s
Lyu Lijun
Yilihamu Yaermaimaiti
Abstract：Aiming at the problems of low detection accuracy and miss and false detection caused by over-lapping or obscured apples in apple surface defects detection,an improved apple surface defect detection method with YOLOv5s algorithm was proposed in this study.Firstly,the convolutional block attention module(CBAM)was added to the Backbone part of the YOLOv5s model to enhance the detection model's attention to the information of important regions of images,so as to improve the model's ability to detect defects on the sur-face of apple.Secondly,a weighted bidirectional feature pyramid network(BiFPN)was introduced to fully in-tegrate the apple surface defect features at different scales in order to reduce missed and false detections.Final-ly,the Soft-NMS algorithm was used instead of the NMS algorithm in the original network to optimize the re-dundant bounding box screening conditions and further reduce the miss detection rate of the model.The experi-mental results showed that the proposed algorithm in this paper achieved 95.5％of mean average precision(mAP),which improved by 3.3 percentage point compared to the original algorithm,and the recall rate was improved by 4.6 percentage points,so it could be better used to detect the surface defects of apples.
Keywords：Apple surface defect detectionYOLOv5sConvolutional attention mechanismWeighted bidirectional feature pyramid network(BiFPN)
Publication Date：2025-06-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：9( 149-157 )