Authors:
Santheep Yesudasu
and
Jean-François Brethé
Affiliation:
GREAH, Normandy University, Le Havre, France
Keyword(s):
Automated Depalletising, Cardboard Package Detection, Keypoint Detection, YOLOv8, Point Cloud Data, 3D Pose Estimation, Robotic Manipulation, Industrial Automation, Deep Learning, Computer Vision.
Abstract:
This paper introduces advanced methods for detecting corners, edges, and gaps and estimating the pose of cardboard packages in automated depalletizing systems. Initially, traditional computer vision techniques such as edge detection, thresholding, and contour detection were used but fell short due to issues like variable lighting conditions and tightly packed arrangements. As a result, we shifted to deep learning techniques, utilizing the YOLOv8 model for superior results. By incorporating point cloud data from RGB-D cameras, we achieved better 3D positioning and structural analysis. Our approach involved careful dataset collection and annotation, followed by using YOLOv8 for keypoint detection and 3D mapping. The system’s performance was thoroughly evaluated through simulations and physical tests, showing significant accuracy, robustness, and operational efficiency improvements. Results demonstrated high precision and recall, confirming the effectiveness of our approach in industria
l applications. This research highlights the potential of using different sensors’ information to feed the deep learning algorithms to advance automated depalletizing technologies.
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