Mobile phone screen surface scratch detection based on optimized YOLOv5 model (OYm). Issue 5 (25th December 2022)

• Record Type: Journal Article
• Authors: Zhao, Jian; Zhu, Bolin; Peng, Mo; Li, Lingling
• Is Part Of: IET image processing. Volume 17:Issue 5(2023)
• Page Start: 1364
• Page End: 1374
• Publication Date: 2022-12-25
• Subjects: BottleneckCSP -- deep learning -- defect detection -- GhostNet -- YOLOv5; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12718
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: To improve phone screen surface detection efficiency, an optimized YOLOv5s model (OYm) based on GhostNet(YOLOv5GHOSTs) and BottleneckCSP is proposed. For a given target sample, OYm could effectively reduce the computation of GFLOPS and detection time by optimizing the network structure. The detection results show that the mean average precision_0.5 (mAP_0.5) exceeds 95%, and the average detection rate is 16 ms. Compared with the traditional YOLOv5s model, the loss of average accuracy is ensured to be controlled within 3%, the detection frame rate of OYm is risen by 56.25%, and GFLOPS is decreased by 64.2%. The principle of OYm is explained in detail, and the proposed model is then experimentally validated. Abstract : To improve the efficiency of screen surface detection, we propose an optimized YOLOv5s model (OYm) based on GhostNet (YOLOv5GHOSTs) and BelannessCSP.