Pallet monitoring system based on a heterogeneous sensor network for transparent warehouse processes

Borstell, Hagen; Kluth, Jewgeni; Jaeschke, Marcel; Plate, Cathrin; Gebert, Bernd; Richter, Klaus

doi:10.1109/sdf.2014.6954718

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…Widespread multistage heterogeneous networks are used in applications such as surveillance [ 8 , 9 , 12 , 26 ] and vehicle detection [ 10 , 11 ]. In this experiment, each

is set randomly in the range of

.…”

Section: Simulation Results Analysismentioning

confidence: 99%

“…In our coverage study, we focus on the minimal accumulation of detected probability on a trajectory.Besides coverage judgment, the presented trajectory-based method can also be good for intrusion detections to preventnetworks against invasion by means of its prediction of the intruding trajectory, especially forsurveillance in military scenarios [ 8 , 9 ]. Under coverage guidance lighted by our assessment results, the sensor network can better serve for other jobs includingvehicle detection [ 10 , 11 ]. Our research accomplishes several tasks and makes the following contributions: Unfreezing the reliant requirement for global network data.…”

Section: Introductionmentioning

confidence: 99%

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A Trajectory-Based Coverage Assessment Approach for Universal Sensor Networks

Qin

Zheng

Tian

2015

Sensors

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To solve the problem of coverage performance assessment, this study proposes an evaluation method based on the trajectory of the target, which is applicable to universal sensor networks, including both heterogeneous and homogeneous sensor networks. Different from the traditional Voronoi algorithm, the proposed Improved Coverage Force Division (ICFD) plans a coverage force division map whichscales the qualitative coverage performancebasedon both covering intensities andlocations of the nodes. Furthermore, the Trajectory-based Evaluating Schedule (TES) is responsible for solving the quantitative coverage evaluationproblem by measuringthe resulting trajectories’ Balance Values (BVs). A model of weak-point ranking conjoined in consideration of coverage force and distance can guide future deployment to compensate coverage. Comparative trials using the greedy algorithm, Voronoi algorithm, and the proposed TES verify that TES achieves the approximate results for two-stage and multistage heterogeneous sensor networks with acceptable difference and lower complexity, and it is superior to the Voronoi algorithm in homogeneous sensor networks interms of breaking the four-point circle block.

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“…Widespread multistage heterogeneous networks are used in applications such as surveillance [ 8 , 9 , 12 , 26 ] and vehicle detection [ 10 , 11 ]. In this experiment, each

is set randomly in the range of

.…”

Section: Simulation Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Trajectory-Based Coverage Assessment Approach for Universal Sensor Networks

Qin

Zheng

Tian

2015

Sensors

View full text Add to dashboard Cite

show abstract

“…A technique for using auxiliary markers, such as QR codes, for position recognition has surfaced in the industry as a solution to such issues [ 13 ]. The pallets are marked with additional markers, and an on-board scanning gun is used to find the markers.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System

Ren

Pan

Yao

et al. 2022

Sensors

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In this research, we present an intelligent forklift cargo precision transfer system to address the issue of poor pallet docking accuracy and low recognition rate when using current techniques. The technology is primarily used to automatically check if there is any pallet that need to be transported. The intelligent forklift is then sent to the area of the target pallet after being recognized. Images of the pallets are then collected using the forklift’s camera, and a deep learning-based recognition algorithm is used to calculate the precise position of the pallets. Finally, the forklift is controlled by a high-precision control algorithm to insert the pallet in the exact location. This system creatively introduces the small target detection into the pallet target recognition system, which greatly improves the recognition rate of the system. The application of Yolov5 into the pallet positional calculation makes the coverage and recognition accuracy of the algorithm improved. In comparison with the prior approach, this system’s identification rate and accuracy are substantially higher, and it requires fewer sensors and indications to help with deployment. We have collected a significant amount of real data in order to confirm the system’s viability and stability. Among them, the accuracy of pallet docking is evaluated 1000 times, and the inaccuracy is kept to a maximum of 6 mm. The recognition rate of pallet recognition is above 99.5% in 7 days of continuous trials.

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“…Borstell et al [Bor+14] present a system for pallet monitoring. They use heterogeneous sensor setup and a system architecture with subsystems for pallet identification, pallet dimensioning, vehicle positioning and load change detection.…”

Section: Tracking and Tracingmentioning

confidence: 99%

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Naumann

Dörr

Salscheider

et al. 2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Computer vision applications in transportation logisticsand warehousing have a huge potential for process automation. We present a structured literature review on research in the field to help leverage this potential. All literature is categorized w.r.t. the application, i.e. the task it tackles and w.r.t. the computer vision techniques that are used. Regarding applications, we subdivide the literature in two areas: Monitoring, i.e. observing and retrieving relevant information from the environment, and manipulation, where approaches are used to analyze and interact with the environment. In addition to that, we point out directions for future research and link to recent developments in computer vision that are suitable for application in logistics. Finally, we present an overview of existing datasets and industrial solutions. We conclude that while already many research areas have been investigated, there is still huge potential for future research. The results of our analysis are also available online at https://a-nau.github.io/cv-in-logistics.1 Verband der Automobilindustrie e.V. (VDA), see https://www.vda.de/en.

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Pallet monitoring system based on a heterogeneous sensor network for transparent warehouse processes

Cited by 9 publications

References 6 publications

A Trajectory-Based Coverage Assessment Approach for Universal Sensor Networks

A Trajectory-Based Coverage Assessment Approach for Universal Sensor Networks

Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

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