Simple and Effective Load Volume Estimation in Moving Trucks using LiDARs

Amorim, Lucas L.; Mutz, Filipe; Souza, Alberto F. De; Badué, Claudine; Oliveira-Santos, Thiago

doi:10.1109/sibgrapi.2019.00036

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…Measuring the volume of payload usually depends on human observation [12], and it can be challenging when the surface of payload is rough [13]. As mentioned before, measuring the volume of payload is vital to ensure high loading rate and prevent overloading that can damage to road during trip.…”

Section: The Proposed Algorithm Recognizes the Truck Dump Body In Rgb...mentioning

confidence: 99%

“…As mentioned before, measuring the volume of payload is vital to ensure high loading rate and prevent overloading that can damage to road during trip. However, performing the measurement of payload for themselves can be inefficient and make work more labor-intensive [13,14].…”

Section: The Proposed Algorithm Recognizes the Truck Dump Body In Rgb...mentioning

confidence: 99%

“…In [1], laser rangefinders data of soil in truck are placed in a 2-D terrain map to plan the dump point. In [13], two lidars that mounted at top and road are used to reconstruct the truck and payload while truck is moving. Then, computer vision algorithms are used to estimate volume of payload.…”

Section: The Proposed Algorithm Recognizes the Truck Dump Body In Rgb...mentioning

confidence: 99%

See 2 more Smart Citations

Automatic Loading Algorithm via Vision-based Payload Volume Estimation

Kwak¹,

Bae²,

Huh³

et al. 2022

Asian Society for Precision Engineering and Nanotechnology (ASPEN 2022)

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Section: The Proposed Algorithm Recognizes the Truck Dump Body In Rgb...mentioning

confidence: 99%

Section: The Proposed Algorithm Recognizes the Truck Dump Body In Rgb...mentioning

confidence: 99%

Section: The Proposed Algorithm Recognizes the Truck Dump Body In Rgb...mentioning

confidence: 99%

See 1 more Smart Citation

Automatic Loading Algorithm via Vision-based Payload Volume Estimation

Kwak¹,

Bae²,

Huh³

et al. 2022

Asian Society for Precision Engineering and Nanotechnology (ASPEN 2022)

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“…Similarly, laser scanning is also applied in the roundwood measurement of truck loads (Nylinder et al, 2008). Light detection and ranging sensors (LiDARs) are gaining popularity and are inexpensive and they have also been used to measure the load on trucks (Amorim et al, 2019).…”

Section: Volume Estimationmentioning

confidence: 99%

A neural network–based approach for fill factor estimation and bucket detection on construction vehicles

Yao

et al. 2021

Computer aided Civil Eng

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Bucket fill factor is of paramount importance in measuring the productivity of construction vehicles, which is the percentage of materials loaded in the bucket within one scooping. Additionally, the locational information of the bucket is also indispensable for scooping trajectory planning. Some research has been conducted to measure it via state-of-the-art computer vision approaches, but their robustness against various environment conditions is not considered. The aim of this study is to fill this gap and six distinctive environment settings are included. Images captured by a stereo camera are used to generate point clouds before being structured into 3D maps. This novel preprocessing pipeline for deep learning is originally proposed and its feasibility has been validated through this study. Moreover, multitask learning is employed to exploit the positive relationship among two tasks: fill factor prediction and bucket detection. Therefore, after preprocessing, 3D maps are forwarded to a faster region with convolutional neural network incorporated with an improved residual neural network. The value of fill factor is acquired via a classification and probabilistic-based approach, which is novel, achieving an inspiring result (overall volume estimation accuracy: 95.23% and detection precision: 92.62%) at the same time.

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“…Related research in the area of three-dimensional (3D) mapping within coal mines [5,6] has validated the application of laser-scanning sensors to the problem of volume estimation of coal targets. Accurate volume and/or surface area mapping of coal targets using lidar technology requires careful selection and configuration of measuring instruments combined with an effective algorithm to deal with complicating factors such as target aspect and related shadowing effects [7], vehicle motion [8], and alterations in vehicle velocity [9]. Various volume-estimation approaches have been trialled in a range of application domains including mining [10], agriculture [11], and forestry [12].…”

Section: Introductionmentioning

confidence: 99%

Remnant Coal Detection System

Hargrave

McPhee

2020

Resources

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This paper describes the development and successful implementation of a system designed to detect coal deposits remaining in coal train wagons after unloading (dumping). These undesirable coal deposits constitute both small residual amounts of “carryback”, but also larger “hang-ups” of significant volume that have failed to discharge. The system was originally developed simply to detect and record volumes of carryback, as part of an effort to characterise the extent of the problem for the coal transport industry, but was then enhanced to provide real-time feedback of large hang-ups so that they could be discharged prior to the wagons exiting the dump station. The paper describes the hardware and processing systems used in the system, including the different strategies employed to ensure a reliable detection system. The system has now been installed and operated in a production environment at three dump stations across two different coal terminals, and a case study of the results from one of these dump stations is presented. Automating remnant coal detection at dump stations provides short interval control to minimise potential hazards and downtime, and historical data that may be integrated into existing data platforms and analysed for productivity, environmental, and safety planning insights.

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Simple and Effective Load Volume Estimation in Moving Trucks using LiDARs

Cited by 12 publications

References 5 publications

Automatic Loading Algorithm via Vision-based Payload Volume Estimation

Automatic Loading Algorithm via Vision-based Payload Volume Estimation

A neural network–based approach for fill factor estimation and bucket detection on construction vehicles

Remnant Coal Detection System

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