Scanning of trucks to produce 3D models for analysis of timber loads

Fojtik, David; Podesva, Petr; Mahdal, Miroslav; Mihola, Milan

doi:10.1109/carpathiancc.2016.7501092

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…For this purpose, a unique gate equipped with a set of 2D lidar scanners and a group of cameras has been developed and installed. Functionality and design of this gate are described in detail in the older article [9].…”

Section: Recording Trucksmentioning

confidence: 99%

Classification of Timber Load on Trucks

Sikora¹,

Fojtik²

2020

Adv. sci. technol. eng. syst. j.

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All trucks heading into the paper mill MONDI, Slovakia, have to pass an automatic security check. It controls if storage of its wood load meets all standards of safety. Each truck is scanned by a group of 2D scanners. After that the inspection of timber load is done by a software with use of the data gained by these scanners. The security software is universal for all kinds of storage of timber loads. This article is dedicated to deal with a problem of classification a kind of wood storage on a semi-trailer. The classification is solved by training a convolutional neural network on datasets with recorded trucks of both kinds to learn patterns distinguishing them. The image classification is done with use of images recorded by a set of cameras. By determining a type of storage, it is possible to execute the safety check for a specific type of wood load with better result than the universal check.

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Section: Recording Trucksmentioning

confidence: 99%

Classification of Timber Load on Trucks

Sikora¹,

Fojtik²

2020

Adv. sci. technol. eng. syst. j.

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“…It is not easy to measure in real time the amount of sand which an excavator load in working environment [1,2]. Previous method to measure this workload in real environment was to measure the charge of weight of dump truck.…”

Section: Introductionmentioning

confidence: 99%

Development of Sand Volume Estimator for Under-Struck Excavator Bucket Using Single Camera

Kim¹,

Lim²,

Jung³

2018

IJFIS

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To support the intelligence of construction environment, it is important to measure the workload of excavator in real time. But, previous studies are expensive to implement or can not processed in real time. In this paper, an image-based method to estimate the workload of the excavator bucket especially for the state of under-struck is proposed by assuming the shape of bucket and the shape of sand inside the bucket as geometric models. By analyzing the relation between single camera image and actual bucket geometry, the volume of sand which is proportional to the excavator workload is estimated. The experimental results show 93.5% accuracy even though only some part of sand region is unseen.

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