Find the centroid: a vision-based approach for optimal object grasping

Abstract

Recently, the booming development of deep learning techniques facilitates remarkable progress in a wide range of applications, has provided insightful cues in today's modern industry and research societies. For instance, the manufacturing and agricultural industries have witnessed the unparallel dominance in AI vision to integrate the production management. Therefore, owing to its impressive learning capability, this article aims to develop an automated system that can integrate data processing and packaging operations. Notably, the inspection system can capture the geometric properties of an ellipsoidal ham and thus can be grasped by the robotic arm. Prior to the centroid detection, a series of instant segmentation procedures is introduced to determine the position of the object, which incorporates both the object localization and semantic segmentation processes. As a result, the proposed model yields a promising segmentation accuracy when experimented on the ham dataset that consists of 293 images. The best Sørensen–Dice coefficient achieved is 91%, which indicates the compelling performance. To further verify the effectiveness of the proposed framework, quantitative and qualitative visualization are presented to demonstrate the quality of the image segmentation task. The proposed algorithm and the raw data are publicly available at https://github.com/jason2303779/Ham-Centroid. Practical Applications: Recently, the booming development of deep learning techniques facilitates remarkable progress in a wide range of applications has provided insightful cues in today's modern industry and research societies. For instance, the manufacturing and agricultural industries have witnessed the unparallel dominance in AI vision to integrate production management. Therefore, this paper aims to develop an automated system that can integrate data processing and packaging operations due to its impressive learning capability. In brief, this paper introduces the first plausible mechanism to precisely identify the geometric center of an irregular-shaped object, viz, ellipsoidal ham. A novel pipeline that composes both the object detection and semantic segmentation architectures is proposed to facilitate the vision understanding process. Succinctly, the inspection system can capture an ellipsoidal ham's geometry properties and thus facilitate the robotics vision for food products, especially in stimulating automatic industrial manufacturing development.