## 2 Literature Review  
Over the last few decades, deep learning--particularly Convolutional Neural Networks (CNNs)--has been extensively applied to non-PPE detection. For instance, [PERSON] et al. [7] adopted Faster R-CNN to automatically detect non-safety helmets, attaining accuracy rates of 90.1-98.4% in diverse scenarios. [PERSON] et al. [8] enhanced Faster R-CNN with multi-scale training and expanded anchor strategies, elevating helmet detection accuracy by 7%. [PERSON] and [PERSON] [9] integrated densely connected convolutional networks into YOLOv3's backbone, preserving detection time while boosting speed by 52% and accuracy by 5.7%. [PERSON] et al. [10] utilized a single-stage face detector to recognize safety helmets, followed by a face-to-helmet regression model. [PERSON] [11] combined YOLOv3 with Bayesian inference (71.23% accuracy), while [PERSON] [12] discovered YOLOv5x to be most accurate (86.55%) and YOLOv5s to be fastest (0.019 s per frame). [PERSON] et al. [13] proposed a YOLACT-based real-time method, achieving 66.4% accuracy for detecting helmets and vests, and [PERSON] et al. [14] enhanced YOLOv5/YOLOv8 with a seahorse optimization algorithm, yielding 66% accuracy for various non-PPE classes (helmets, masks, gloves, vests, and shoes).  
Although these studies reported impressive findings, most relied on unaugmented data, leaving the role of image augmentation largely unexplored. Meanwhile, several construction-oriented investigations have leveraged augmentation to enhance accuracy. For example, [PERSON] et al. [15] boosted resource-detection rates from 35.96% to 60.22% by combining removing-and-inpainting, cut-and-paste, and image-variation methods with Mask R-CNN--though, notably, using only cut-and-paste lowered accuracy compared to the raw dataset, whereas other single techniques improved it. [PERSON] et al. [6] introduced brightness, scale, blur, perspective, and rotation for rebar counting, showing up to a 5.43% improvement in accuracy using Faster R-CNN under different hyperparameter settings. [PERSON] et al. [16] explored gamma transformation, Gaussian blur, and salt-and-pepper noise for heavy-equipment detection with YOLO, observing that individual transformations sometimes reduced training accuracy while marginally improving test accuracy, yet combining all techniques raised both training and test accuracy.  
Beyond construction-focused tasks, other research illustrates the nuanced effects of augmentation. [PERSON] [17] examined nine techniques--brightness, hue, saturation, contrast, scale, translation, cutout, weather, and various combinations--for two tasks (window detection and window-state detection) in building-facade images. These were demon strated with three object detection methods (Faster R-CNN, YOLO, and R-FCN) using five architectures: ResNet-50, ResNet-101, MobileNetV3, EfficientNetV2, and InceptionV3, revealing that mixing multiple approaches did not always outperform a single well-chosen technique. [PERSON] et al. [18] applied rotation, horizontal flipping, Gaussian blur, and brightness adjustments to detect building entrances in street view images; model accuracy rose from 61.2% (original data) to 85.92% (rotation alone), 86.27% (three techniques), and 87.42% (all techniques). Collectively, these findings confirm that individual or combined augmentation strategies can outperform unaugmented data, though merging multiple methods is not invariably superior [19; 20]. This underscores the importance of exploring augmentation approaches in different and underexplored contexts, including non-PPE detection on construction sites--a topic that remains largely unexamined.