Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method
In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the oper...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/7/2859 |
| _version_ | 1797212903733985280 |
|---|---|
| author | Young-Jun Park Chang-Yong Yi |
| author_facet | Young-Jun Park Chang-Yong Yi |
| author_sort | Young-Jun Park |
| collection | DOAJ |
| description | In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-twelfth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model’s learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring. |
| first_indexed | 2024-04-24T10:49:47Z |
| format | Article |
| id | doaj.art-4bc33a2f4acb4b9e97ff316a168a1997 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:49:47Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4bc33a2f4acb4b9e97ff316a168a19972024-04-12T13:15:01ZengMDPI AGApplied Sciences2076-34172024-03-01147285910.3390/app14072859Time of Flight Distance Sensor–Based Construction Equipment Activity Detection MethodYoung-Jun Park0Chang-Yong Yi1Intelligent Construction Automation Center, Kyungpook National University, Daegu 41566, Republic of KoreaIntelligent Construction Automation Center, Kyungpook National University, Daegu 41566, Republic of KoreaIn this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-twelfth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model’s learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring.https://www.mdpi.com/2076-3417/14/7/2859ToF distance sensorequipment activity recognitionDCNN classificationpiston movementdata transformation |
| spellingShingle | Young-Jun Park Chang-Yong Yi Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method Applied Sciences ToF distance sensor equipment activity recognition DCNN classification piston movement data transformation |
| title | Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method |
| title_full | Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method |
| title_fullStr | Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method |
| title_full_unstemmed | Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method |
| title_short | Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method |
| title_sort | time of flight distance sensor based construction equipment activity detection method |
| topic | ToF distance sensor equipment activity recognition DCNN classification piston movement data transformation |
| url | https://www.mdpi.com/2076-3417/14/7/2859 |
| work_keys_str_mv | AT youngjunpark timeofflightdistancesensorbasedconstructionequipmentactivitydetectionmethod AT changyongyi timeofflightdistancesensorbasedconstructionequipmentactivitydetectionmethod |