Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations
The rapid growth of Deep Learning techniques plays a vital role in automation of manual work in various areas. One such area for application of new technology is that of Construction Worker Safety. It has thus become imperative to improve existing systems with the new capabilities of technology. Thi...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10835085/ |
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| author | Yash Seth M. Sivagami |
| author_facet | Yash Seth M. Sivagami |
| author_sort | Yash Seth |
| collection | DOAJ |
| description | The rapid growth of Deep Learning techniques plays a vital role in automation of manual work in various areas. One such area for application of new technology is that of Construction Worker Safety. It has thus become imperative to improve existing systems with the new capabilities of technology. This paper discusses a methodology of improving the performance of an existing approach of object detection, YOLOv8. The proposed work comprises of improved training of model and detection of helmet in worker images, using Test Time Augmentation (TTA) based approach. Image Transformations such as Histogram Equalization, Gamma Correction, Gaussian Blurring and Contrast Stretching are applied to augment the dataset by creating more versions of the existing data. This has shown to improve the performance of the model and also generalize better by preventing overfitting. A Test Time Augmentation-based Confidence Thresholding formula (TTACT) is also proposed, to improve the performance of helmet detection. |
| format | Article |
| id | doaj-art-80d2b970373849969d4a92c7b5c5f7a7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-80d2b970373849969d4a92c7b5c5f7a72025-01-21T00:00:53ZengIEEEIEEE Access2169-35362025-01-0113105821059410.1109/ACCESS.2025.352751110835085Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image TransformationsYash Seth0https://orcid.org/0009-0002-1734-6879M. Sivagami1https://orcid.org/0000-0001-8621-5800School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaThe rapid growth of Deep Learning techniques plays a vital role in automation of manual work in various areas. One such area for application of new technology is that of Construction Worker Safety. It has thus become imperative to improve existing systems with the new capabilities of technology. This paper discusses a methodology of improving the performance of an existing approach of object detection, YOLOv8. The proposed work comprises of improved training of model and detection of helmet in worker images, using Test Time Augmentation (TTA) based approach. Image Transformations such as Histogram Equalization, Gamma Correction, Gaussian Blurring and Contrast Stretching are applied to augment the dataset by creating more versions of the existing data. This has shown to improve the performance of the model and also generalize better by preventing overfitting. A Test Time Augmentation-based Confidence Thresholding formula (TTACT) is also proposed, to improve the performance of helmet detection.https://ieeexplore.ieee.org/document/10835085/Construction worker safetydata augmentationdeep learningimage transformationobject detectiontest time augmentation |
| spellingShingle | Yash Seth M. Sivagami Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations IEEE Access Construction worker safety data augmentation deep learning image transformation object detection test time augmentation |
| title | Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations |
| title_full | Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations |
| title_fullStr | Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations |
| title_full_unstemmed | Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations |
| title_short | Enhanced YOLOv8 Object Detection Model for Construction Worker Safety Using Image Transformations |
| title_sort | enhanced yolov8 object detection model for construction worker safety using image transformations |
| topic | Construction worker safety data augmentation deep learning image transformation object detection test time augmentation |
| url | https://ieeexplore.ieee.org/document/10835085/ |
| work_keys_str_mv | AT yashseth enhancedyolov8objectdetectionmodelforconstructionworkersafetyusingimagetransformations AT msivagami enhancedyolov8objectdetectionmodelforconstructionworkersafetyusingimagetransformations |