Architectures and applications of high-speed vision

Watanabe, Yoshihiro; Oku, Hiromasa; Ishikawa, Masatoshi

doi:10.1007/s10043-014-0140-8

Cited by 18 publications

(8 citation statements)

References 34 publications

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“…• Images must be acquired and processed in less than 300ms to satisfy production cadence requirements. • The inline inspection system has to process the whole upper surface of the pizza package and hence a highspeed system that provides high-resolution images is required [27]- [29]. With all these information in mind, we propose a computer vision system composed of hardware and software components presented in next sections.…”

Section: Packaging Machinesmentioning

confidence: 99%

“…Obviously, all these components are specifically designed to meet the restrictions imposed by the features of the product and the quality requirements [24]- [26]. Moreover, in real scenarios, an extra effort is required to properly synchronize all software components with the industrial hardware to accept or reject products in real time [27]- [29]. In this paper, we have focused on the quality control of pizza packages.…”

Section: Introductionmentioning

confidence: 99%

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A Deep-Learning Based Solution to Automatically Control Closure and Seal of Pizza Packages

Banús¹,

Boada

Bardera

et al. 2021

IEEE Access

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Closure and seal inspection is one of the key steps in quality control of pizza packages. This is generally carried out by human operators that are not able to inspect all the packages due to cadence restrictions. To overcome this limitation, a computer vision system that automatically performs 100% inline seal and closure inspection is proposed. In this paper, after evaluating pizza package features, the manual quality control procedure, and the packaging machines of a real industrial scenario, a detailed description of hardware and software components of the proposed system as well as the main design decisions are presented. Focusing on the hardware, line-scan technology and hyperspectral imaging has been considered to ensure that all relevant information can be acquired independently of the pizza brand, topping, and film features. Focusing on the software, this applies a three-phases strategy that, first, applies a set of basic rejection controls; second, identifies the sealing region; and third, prepares the data for prediction through the classification of the pizzas using a deep learning network. This network is one of the software key elements and has been selected after comparing the commercial off-the-shelf (pretrained-dl-classifier-resnet50 from MVTec Halcon) and the custom-developed (ResNet18) architectures designed to automate the accept/reject classification of pizza packages. To train the networks, a classification of pizza package defects, focusing on sealing and closure, and an image-based method able to automatically detect them have been proposed. The system has been tested in laboratory and in real industrial conditions comparing it with the manual scenario and considering three pizza brands with two toppings per brand. From the evaluation, it has been seen that ResNet18 achieves the best results with mean, maximum, and minimum precision values of 99.87%, 99.95%, and 99.74%, respectively. Moreover, our system achieves twice the throughput rate with respect to the manual scenario, with the guarantee that all pizzas are evaluated, which is not possible in the manual scenario due to operator fatigue. The proposed solution can be easily adapted to similar contexts, even considering packages with other shapes.

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Section: Packaging Machinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep-Learning Based Solution to Automatically Control Closure and Seal of Pizza Packages

Banús¹,

Boada

Bardera

et al. 2021

IEEE Access

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“…This paper applies the axiomatic design method [5] to explicitly describe the coupling of design parameters, and to link them to functional requirements. An example that involves design parameters from four domains, and links them to two functional requirements, is described in (1) and (2). (1)…”

Section: A Overview Of Cross-domain Modeling Frameworkmentioning

confidence: 99%

“…High-speed visual feedback is increasingly used in mechatronics and robotics systems [1] [2]. Visual servo systems are an important category of these systems, and use visual feedback in closed-loop control [3].…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Modeling and Optimization of High-Speed Visual Servo Systems

Corporaal

Jonker

et al. 2018

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

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“…Despite object tracking being a major research topic in computer vision, applicability has been limited by the low camera dynamics of standard vision sensors. Increasing the frame rate only burdens the computation techniques [2]- [4], preventing a dynamic, low-latency formulation of object tracking. On the other hand, the discontinuous motion information captured using a standard 30 fps video camera is an obvious disadvantage for frame-based object tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

e-TLD: Event-Based Framework for Dynamic Object Tracking

Ramesh

Zhang

Yang

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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This paper presents a long-term object tracking framework with a moving event camera under general tracking conditions. A first of its kind for these revolutionary cameras, the tracking framework uses a discriminative representation for the object with online learning, and detects and re-tracks the object when it comes back into the field-of-view. One of the key novelties is the use of an event-based local sliding window technique that tracks reliably in scenes with cluttered and textured background. In addition, Bayesian bootstrapping is used to assist real-time processing and boost the discriminative power of the object representation. On the other hand, when the object re-enters the field-of-view of the camera, a data-driven, global sliding window detector locates the object for subsequent tracking. Extensive experiments demonstrate the ability of the proposed framework to track and detect arbitrary objects of various shapes and sizes, including dynamic objects such as a human. This is a significant improvement compared to earlier works that simply track objects as long as they are visible under simpler background settings. Using the ground truth locations for five different objects under three motion settings, namely translation, rotation and 6-DOF, quantitative measurement is reported for the event-based tracking framework with critical insights on various performance issues. Finally, real-time implementation in C++ highlights tracking ability under scale, rotation, view-point and occlusion scenarios in a lab setting.

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Architectures and applications of high-speed vision

Cited by 18 publications

References 34 publications

A Deep-Learning Based Solution to Automatically Control Closure and Seal of Pizza Packages

A Deep-Learning Based Solution to Automatically Control Closure and Seal of Pizza Packages

Cross-Domain Modeling and Optimization of High-Speed Visual Servo Systems

e-TLD: Event-Based Framework for Dynamic Object Tracking

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