Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model

Yang, Hualin; Chen, Long; Chen, Miaoting; Ma, Zongwen; Deng, Fang; Li, Maozhen; Li, Xiangrong

doi:10.1109/access.2019.2958614

Cited by 85 publications

(55 citation statements)

References 21 publications

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“…The YOLOv3 technique with a modification to curtail floating point operation (FLOP) was modified by [ 19 ], which conducted a performance increase 250% faster. YOLOv3 also uses K-means in each target to distinguish the clusters, and provides the accuracy more than 90% [ 20 ]. However, YOLOv3 requires a fast GPU and computer, implying that not only high specification hardware is needed, but also some detection approaches solely focus on the confidence value.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Muslikhin

Horng

Yang

et al. 2021

Sensors

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In this study, an Artificial Intelligence of Things (AIoT)-based automated picking system was proposed for the development of an online shop and the services for automated shipping systems. Speed and convenience are two key points in Industry 4.0 and Society 5.0. In the context of online shopping, speed and convenience can be provided by integrating e-commerce platforms with AIoT systems and robots that are following consumers’ needs. Therefore, this proposed system diverts consumers who are moved by AIoT, while robotic manipulators replace human tasks to pick. To prove this idea, we implemented a modified YOLO (You Only Look Once) algorithm as a detection and localization tool for items purchased by consumers. At the same time, the modified YOLOv2 with data-driven mode was used for the process of taking goods from unstructured shop shelves. Our system performance is proven by experiments to meet the expectations in evaluating efficiency, speed, and convenience of the system in Society 5.0’s context.

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Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Muslikhin

Horng

Yang

et al. 2021

Sensors

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“…YOLO (You Look Once), algorithm proposed by RedMon and Grishick [52], has the ability to detect objects by regression and location exclusion and classification of the object from one end to the other by a single parsing. This makes it position itself at the top for speed algorithms, but with a low degree of accuracy in the case of small objects and an error rate in the case of pedestrian scenes with a high degree of complexity [53,54].…”

Section: Description Pedestrian Setup and Practical Scenariosmentioning

confidence: 99%

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Zadobrischi

Dimian

2021

Remote Sensing

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Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management of dangerous situations, with the help of systems and modules designed in this direction. The approach and classification of situations that can cause accidents is another feature analyzed in this paper, including detecting elements of a psychosomatic nature: analysis and detection of the conditions a driver goes through, pedestrian analysis, and maintaining a preventive approach, all of which are embedded in a modular architecture. The versatility and usefulness of such a system come through its ability to adapt to context and the ability to communicate with traffic safety systems such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-everything), and VLC (visible light communication). All these elements are found in the operation of the system and its ability to become a portable device dedicated to road safety based on (radio frequency) RF-VLC (visible light communication).

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“…Deep learning has developed rapidly. Many researchers have begun to use network models, such as Faster R-CNN [10] and YOLO [11], to identify The physical properties of tea were measured at the Lishui Comprehensive Test Station of the National Tea Industry Technology System on 24 October 2020. As shown in Figures 2 and 3, the measurement samples of the first five physical characteristics are 100 fresh tea leaves picked randomly, and the tea variety is tulip.…”

Section: Introductionmentioning

confidence: 99%

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Zhu

Tong

et al. 2021

Agriculture

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Accurately obtaining the posture and spatial position of tea buds through machine vision and other technologies is difficult due to the small size, different shapes, and complex growth environment of tea buds. Therefore, end effectors are prone to problems, such as picking omission and picking error. This study designs a picking end effector based on negative pressure guidance for famous tea. This end effector uses negative pressure to guide tea buds in a top-down manner, thereby correcting their posture and spatial position. Therefore, the designed end effector has deviation tolerance performance that can improve the picking success rate. The pre-experiment is designed, the tip of apical bud is referred to as the descent position, and the negative pressure range is determined to be 0.6 to 0.9 kPa. A deviation tolerance orthogonal experiment is designed. Experimental results show that various experimental factors are ranked in terms of the significance level of the effect on the average success rate, and the significance ranking is as follows: negative pressure (P) > pipe diameter (D) > descent speed (V). An evaluation method of deviation tolerance performance is presented, and the optimal experiment factor-level combination is determined as: P = 0.9 kPa, D = 34 mm, V = 20 mm/s. Within the deviation range of a 10 mm radius, the average success rate of the negative pressure guidance of the end effector is 97.36%. The designed end effector can be applied to the intelligent picking of famous tea. This study can provide a reference for the design of similar picking end effectors for famous tea.

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Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model

Cited by 85 publications

References 21 publications

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

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