A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis
This study aims to propose a practical and realistic synthetic data generation method for object recognition in hazardous and data-scarce environments, such as construction sites. Artificial intelligence (AI) applications in such dynamic domains require domain-specific datasets, yet collecting real-...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/9/1447 |
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| author | Jaemin Kim Ingook Wang Jungho Yu Seulki Lee |
| author_facet | Jaemin Kim Ingook Wang Jungho Yu Seulki Lee |
| author_sort | Jaemin Kim |
| collection | DOAJ |
| description | This study aims to propose a practical and realistic synthetic data generation method for object recognition in hazardous and data-scarce environments, such as construction sites. Artificial intelligence (AI) applications in such dynamic domains require domain-specific datasets, yet collecting real-world data can be challenging due to safety concerns, logistical constraints, and high labor costs. To address these limitations, we introduce object range expansion synthesis (ORES), a lightweight and non-generative method for generating synthetic image data by inserting real object masks into varied background scenes using open datasets. ORES synthesizes new scenes, while preserving scale and ground alignment, enabling controllable and realistic data augmentation. A dataset of 30,000 synthetic images was created using the proposed method and used to train an object recognition model. When tested on real-world construction site images, the model achieved a mean average precision at IoU 0.50 (mAP50) of 98.74% and a recall of 54.55%. While recall indicates room for improvement, the high precision highlights the practical value of synthetic data in enhancing model performance without requiring extensive field data collection. This research contributes a scalable approach to data generation in safety-critical and data-deficient environments, reducing dependence on direct data acquisition, while maintaining model efficacy. It provides a foundation for accelerating the deployment of AI technologies in high-risk industries by overcoming data bottlenecks and supporting real-world applications through practical synthetic augmentation. |
| format | Article |
| id | doaj-art-4f8cc9d309df49d1a0bdfb9148ed5369 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-4f8cc9d309df49d1a0bdfb9148ed53692025-08-20T01:50:45ZengMDPI AGBuildings2075-53092025-04-01159144710.3390/buildings15091447A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion SynthesisJaemin Kim0Ingook Wang1Jungho Yu2Seulki Lee3Department of Architecture Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Architecture Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Architecture Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Architecture Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaThis study aims to propose a practical and realistic synthetic data generation method for object recognition in hazardous and data-scarce environments, such as construction sites. Artificial intelligence (AI) applications in such dynamic domains require domain-specific datasets, yet collecting real-world data can be challenging due to safety concerns, logistical constraints, and high labor costs. To address these limitations, we introduce object range expansion synthesis (ORES), a lightweight and non-generative method for generating synthetic image data by inserting real object masks into varied background scenes using open datasets. ORES synthesizes new scenes, while preserving scale and ground alignment, enabling controllable and realistic data augmentation. A dataset of 30,000 synthetic images was created using the proposed method and used to train an object recognition model. When tested on real-world construction site images, the model achieved a mean average precision at IoU 0.50 (mAP50) of 98.74% and a recall of 54.55%. While recall indicates room for improvement, the high precision highlights the practical value of synthetic data in enhancing model performance without requiring extensive field data collection. This research contributes a scalable approach to data generation in safety-critical and data-deficient environments, reducing dependence on direct data acquisition, while maintaining model efficacy. It provides a foundation for accelerating the deployment of AI technologies in high-risk industries by overcoming data bottlenecks and supporting real-world applications through practical synthetic augmentation.https://www.mdpi.com/2075-5309/15/9/1447synthetic dataartificial intelligence performancemachine learningconstruction sites |
| spellingShingle | Jaemin Kim Ingook Wang Jungho Yu Seulki Lee A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis Buildings synthetic data artificial intelligence performance machine learning construction sites |
| title | A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis |
| title_full | A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis |
| title_fullStr | A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis |
| title_full_unstemmed | A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis |
| title_short | A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis |
| title_sort | practical image augmentation method for construction safety using object range expansion synthesis |
| topic | synthetic data artificial intelligence performance machine learning construction sites |
| url | https://www.mdpi.com/2075-5309/15/9/1447 |
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