Single-camera-based sand volume estimation of an excavator bucket

Kim, In-Hwan; Lim, Dong-Woo; Jung, Jin‐Woo

doi:10.1007/s11042-019-7225-0

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…The methodological framework used in this study has not only achieved impressive results in extreme environments but has also been validated in normal environments. For fill factor estimation, I. H. Kim et al (2019) showed an estimation accuracy of 93.7% in a single normal environment.…”

Section: Comparison Of Resultsmentioning

confidence: 98%

“…For fill factor estimation, I. H. Kim et al. (2019) showed an estimation accuracy of 93.7% in a single normal environment. In contrast, Guevara et al.…”

Section: Resultsmentioning

confidence: 99%

“…Through the information of the external sensor, the volume of the material in the bucket can be directly and easily obtained, which can intuitively reflect whether the bucket is full. Methods to obtain volumetric information using external perceptual sensors are classified into light detection and ranging (LiDAR; Woolard & Colby, 2002) and visual cameras (Anwar et al., 2014; I. H. Kim et al., 2019). However, it is essential to note that this does not mean that the estimation of material weight is meaningless, and proprioceptive sensors give accurate estimates of the material weight.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Guan

Chen

Wang

et al. 2022

Computer aided Civil Eng

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The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).

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Section: Comparison Of Resultsmentioning

confidence: 98%

“…For fill factor estimation, I. H. Kim et al. (2019) showed an estimation accuracy of 93.7% in a single normal environment. In contrast, Guevara et al.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Guan

Chen

Wang

et al. 2022

Computer aided Civil Eng

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“…Few deep learning-based approaches for volume estimation on construction field are available in the literature, which is also one of the reasons why the aim of this study is to fill this gap. More importantly, in the general vision-based volume estimation field (Kim et al, 2019;Okinda et al, 2020;Tan et al, 2020), most methods can only achieve accuracy around 90% under merely one specific environment condition (whereas our system is able to maintain an accuracy over 93% across all six conditions).…”

Section: Introductionmentioning

confidence: 87%

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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A bucket fill factor estimation method in construction environments by fusing deep learning and machine vision

Guan,

Wang,

Chen

et al. 2023

Journal of Cleaner Production

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Single-camera-based sand volume estimation of an excavator bucket

Cited by 7 publications

References 2 publications

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

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

A bucket fill factor estimation method in construction environments by fusing deep learning and machine vision

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