LPHD: Line Perspective Hexagonal Screw Detection

In industrial production, hexagonal screws are widely used as fasteners, and it is crucial to have fast and accurate inspection of these screws for automated screwing processes. However, this remains a challenge due to various factors such as environmental distractions, lighting, shadows, and screw...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Chinese Control Conference S. 1 - 6
Hauptverfasser: Ma, Jun, Li, Wenjie, Liu, Xiangbei, He, Cheng, Liu, Zhidong, Guo, Xian
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Technical Committee on Control Theory, Chinese Association of Automation 24.07.2023
Schlagworte:
Approximation algorithms
Computer Vision
Fasteners
Image edge detection
Instance Segmentation
Lighting
Manuals
Production
Robot vision systems
Screw Detection
ISSN:1934-1768
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In industrial production, hexagonal screws are widely used as fasteners, and it is crucial to have fast and accurate inspection of these screws for automated screwing processes. However, this remains a challenge due to various factors such as environmental distractions, lighting, shadows, and screw positioning. To address this, the Line Perspective Hexagonal Screw Detection (LPHD) algorithm has been proposed in this paper, which introduces four innovative techniques: canny value, hexagonal base, perspective correction, and double boundary. The LPHD algorithm starts by feeding the image captured by the robot camera into the YOLO model to obtain the bounding box of the hexagonal screw. Then, the result is subjected to canny edge detection and LSD/FLD line detection. Since the head of a hexagonal screw is a regular hexagonal prism, only three edges are needed to determine a hexagonal contour, which is referred to as the hexagonal base. Perspective correction is proposed to eliminate the camera's perspective effect. If multiple contours are found, a combination of canny value and double boundary is used to identify the optimal result. Ultimately, the LPHD algorithm is applied to find the hexagonal contour in this box. This paper also compares the efficiency and accuracy of LPHD with Mask R-CNN. The results show that LPHD is nearly three times more efficient than the Mask R-CNN algorithm with approximately the same accuracy. Therefore, the LPHD algorithm is a promising solution for detecting hexagonal screws in industrial settings.
ISSN:1934-1768
DOI:10.23919/CCC58697.2023.10239720