Jensen-Shannon Divergence You Only Look Once: A Real-Time Robotic Grasp Detection Network

In this article, the arbitrary-oriented object detection problem with application in robotic grasping is addressed. A novel Jensen-Shannon divergence (JSD)- You Only Look Once (YOLO) model is proposed, which enables real-time grasp detection with high performance. The one-stage object detection network YOLOv5 is modified with a decoupled head, which solves the angle classification problem and rectangle parameter regression problem separately, such that the YOLOv5 network is applicable for robotic grasping and the detection accuracy is significantly improved. A circular smooth label angle classification method is proposed to tackle the boundary discontinuity problem in angle regression, and the periodicity of the angle prediction is guaranteed. A novel Jensen-Shannon intersection of union is designed to calculate the intersection over union of oriented rectangles, which aims to better measure the discrepancies between the prediction and the ground truth and to avoid the singularity problem when two rectangles are not overlapped. Extensive evaluation on the Cornell and visual manipulation relationship dataset datasets demonstrates the effectiveness of the JSD-YOLO model in general robotic grasp operations, with 99.7% and 95.7% image-wise split accuracy, respectively. In this article, the arbitrary-oriented object detection problem is addressed, and a real-time robotic grasp detection network JSD-You Only Look Once (YOLO)model is proposed. A decoupled head is designed, making YOLOv5 network applicable for robotic grasping. A novel Jensen-Shannon intersection of union is proposed to calculate the intersection over union of oriented rectangles. JSD-YOLO model is tested on Cornell and visual manipulation relationship dataset datasets, with 99.7% and 95.7% image-wise split accuracy, respectively.image (c) 2024 WILEY-VCH GmbH