# Analysis of Flame Evolution Generated from Methyl Laurate Droplet Using Deep Learning
## 2 Materials and Methods
### YOLOv9  
This research employs YOLOv9, a more sophisticated iteration of the YOLO (You Only Look Once) object identification model, initially developed by [PERSON] [25]. YOLOv9 is one of the versions of the acclaimed real-time object detection and image segmentation model, YOLO (You Only Look Once). Developed by Ultralytics, the creators of the influential YOLOv5 model, YOLOv9 is built on cutting-edge advancements in deep learning and computer vision. It offers unparalleled performance in terms of speed and accuracy [39], YOLOv9 can be used for a variety of tasks including object detection, image classification, and instance segmentation. The model is under active development, with Ultralytics working on new features and responding to feedback from the community. YOLOv9 has been applied in various fields such as welding quality detection [40], maritime target identification [41], fire detection technology [42], steel surface defect detection [43], and transportation infrastructure [44].  
YOLOv9, a new version of the YOLO object detection model, was introduced in February 2024. It uses programmable gradient information (PGI) and a generalized efficient layer aggregation network (GELAN) to reduce information loss and improve efficiency. YOLO is a single-stage detector that analyzes an image once. It has a backbone, neck, and head, as illustrated in Figure 2. The backbone collects features, the neck combines features, and the head detects objects. YOLOv9 also has an auxiliary section that helps with training by providing additional information. This auxiliary can be removed during inference to speed up the model without affecting accuracy [45].  
In this study, the droplet and flame detection method employed a single object-based detection approach along with an automatic image segmentation strategy. The dataset was acquired using a digital camera and comprises 1500 images of droplets and flames each, which were divided into 20% for testing and 80% for training.  
Figure 3 illustrates relatively the same situations for different conditions. Figure 3a shows the camera focusing on the flame, and Figure 3b shows the camera focusing on the droplets. The second situation can be achieved by providing light from the background so that the light and shape of the fire can be ignored. Meanwhile, to determine the shape of a flame (first situation), the flame was isolated from external light interference so that the only source of lighting was the flame.  
Figure 2: YOLOv9 structure.  
The YOLOv9 model was implemented using the Ultralytics framework with pretrained weights from the COCO dataset for transfer learning. The key steps were as follows:  
* Hardware configuration: Training was performed on an AMD Ryzen 7 5800H CPU with 16 GB of RAM and an NVIDIA GeForce RTX 3060 GPU (6 GB VRAM) to accelerate computation.
* Hyperparameters: The model was configured with a batch size of 16, learning rate of 0.001, and momentum of 0.937 (Table 2). The input image dimensions were fixed at 640\(\times\)640 pixels to balance resolution and computational efficiency.
* Validation metrics: Model performance was evaluated using mAP (0.5-0.95 IoU), precision (P), and recall (R). For flame segmentation, the model achieved a precision of 0.979 and recall of 0.981, indicating robust detection even under dynamic flame conditions (Figure 6).
* Mitigating overfitting: Data augmentation techniques (e.g., rotation, scaling, and contrast adjustment) were applied to the training dataset. Early stopping was employed to terminate training if validation loss plateaued for 50 epochs.  
The use of mAP and IoU for evaluating segmentation accuracy is consistent with state-of-the-art practices in combustion diagnostics [46].