Deep learning methods have recently exhibited impressive performance in object detection. However, such methods needed much training data to achieve high recognition accuracy, which was time-consuming and required considerable manual work like labeling images. In this paper, we automatically prepare training data using robots. Considering the low efficiency and high energy consumption in robot motion, we proposed combining robotic in-hand observation and data synthesis to enlarge the limited data set collected by the robot. We first used a robot with a depth sensor to collect images of objects held in the robot's hands and segment the object pictures. Then, we used a copy-paste method to synthesize the segmented objects with rack backgrounds. The collected and synthetic images are combined to train a deep detection neural network. We conducted experiments to compare YOLOv5x detectors trained with images collected using the proposed method and several other methods. The results showed that combined observation and synthetic images led to comparable performance to manual data preparation. They provided a good guide on optimizing data configurations and parameter settings for training detectors. The proposed method required only a single process and was a low-cost way to produce the combined data. Interested readers may find the data sets and trained models from the following GitHub repository: github.com/wrslab/tubedet
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
Imperial College（帝国理工学院）；University of Sheffield（谢菲尔德大学）；University of Manchester（曼彻斯特大学）；University of Southampton（南安普顿大学）；Fudan University（复旦大学）；Iowa State University（爱荷华州立大学）
Optimizing Drug Delivery in Smart Pharmacies: A Novel Framework of Multi-Stage Grasping Network Combined with Adaptive Robotics Mechanism
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science（中国科学院苏州生物医学工程技术研究所）；Fuzhou University（福州大学）；Monash University（莫纳什大学）；The First Affiliated Hospital, Nanjing Medical University（南京医科大学第一附属医院）
Object Detection Using Sim2Real Domain Randomization for Robotic Applications
Eötvös Loránd Research Network（厄特沃什·罗兰研究网络）；Institute for Computer Science and Control（计算机科学与控制研究所）；Eötvös Loránd University（厄特沃什·罗兰大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Pavol Jozef Šafárik University（帕沃尔·约瑟夫·沙法里克大学）
在非结构化环境中工作的机器人必须能够感知和解释周围环境。机器人领域基于深度学习的模型的主要障碍之一是缺乏针对不同工业应用的特定领域标记数据。在这篇文章中，我们提出了一种基于域随机化的sim 2 real迁移学习方法用于目标检测，可以自动生成任意大小和对象类型的标记合成数据集。随后，训练最先进的卷积神经网络YOLOv 4来检测不同类型的工业对象。使用所提出的域随机化方法，我们可以将现实差距缩小到令人满意的水平，在zero-shot和one-shot传输的情况下，在包含190张真实图像的手动注释数据集上，分别实现86.32%和97.38%的mAP 50分数。我们的解决方案适合工业应用，因为在GeForce RTX 2080 Ti GPU上，每张图像的数据生成过程不到0.5秒，训练仅持续约12小时。此外，它可以通过仅访问一个真实图像进行训练来可靠地区分类似的对象类别。据我们所知，这是迄今为止唯一满足这些限制的工作。
FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
United Arab Emirates University（阿联酋大学）；University at Albany — State University of New York（纽约州立大学奥尔巴尼分校）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Mongolian University of Science and Technology（蒙古科技大学）；National Center for High-Performance Computing（国家高速网络与计算中心）
本文构建开放的 FishEye8K 鱼眼道路目标检测数据集，含 8,000 张图像、157K 个边界框，并评估多种 YOLO 系列 SoTA 模型。
OpenTie: Open-vocabulary Sequential Rebar Tying System
Hong Kong Center for Construction Robotics, The Hong Kong University of Science and Technology（香港建设机器人中心，香港科技大学）；Division of Business and Hospitality Management (BHM), College of Professional and Continuing Education (CPCE), The Hong Kong Polytechnic University (PolyU)（专业及持续教育学院（CPCE）商务及酒店管理系，香港理工大学）
Replication Study and Benchmarking of Real-Time Object Detection Models
Département de physique, de génie physique et d’optique, Université Laval（物理、物理工程与光学系，拉瓦尔大学）；Département de génie électrique et de génie informatique, Université Laval（电气工程与计算机工程系，拉瓦尔大学）；Département d’informatique et de génie logiciel, Université Laval（计算机科学与软件工程系，拉瓦尔大学）
Synthetic Data Generation and Vision-based Wrinkle and Keypoint Detection for Bimanual Cloth Manipulation
Department of Engineering, University of Luxembourg（卢森堡大学工程系）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Université de Lorraine, Arts et Metiers Institute of Technology, LCFC（洛林大学，艺术与工艺技术学院，LCFC）
Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse
INESC TEC-Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência（INESC TEC——系统与计算机工程、技术与科学研究所）；Faculty of Engineering, University of Porto（波尔图大学工程学院）；Faculty of Sciences, University of Porto（波尔图大学理学院）；Khalifa University of Science and Technology（哈利法科学技术大学）
Yolo-Key-6D: Single Stage Monocular 6D Pose Estimation with Keypoint Enhancements
ASELSAN, Aerospace Technologies, Türkiye（ASELSAN航空航天技术，土耳其）；Istanbul Technical University, Department of Computer Engineering, Türkiye（伊斯坦布尔技术大学计算机工程系，土耳其）；New York University Abu Dhabi, Division of Engineering, UAE（纽约大学阿布扎比分校工程学院，阿联酋）
