# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 3 Results
### Evaluation Metrics  
The main research objective is to classify instances as either positive or negative, where fire and non-fire instances are categorized accordingly. In this context, there are four possible scenarios: True Positive, accurately predicting fires; True Negative, correctly predicting non-fires; False Positive, erroneously identifying non-fires as fires; and False Negative, misclassifying fires as non-fires.  
Accuracy serves as a crucial indicator for assessing the model's predictive performance, quantified by calculating the ratio of True Positive and True Negative instances to the total expected positive and negative instances. The calculation method is as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}.\]  
Recall, alternatively termed sensitivity or True Positive Rate, assesses the model's ability to identify positive instances compared to the actual positives. In forest fire detection, a high recall signifies the algorithm's proficiency in identifying fire occurrences accurately. The calculation method is as follows:  
\[Recall=\frac{TP}{TP+FN}.\]  
Precision, also known as Positive Predictive Value, measures the ratio of True Positive instances among those labeled as positive. In forest fire detection, high precision suggests  
\begin{table}
\begin{tabular}{c c} \hline
**Device** & **Configuration** \\ \hline CPU & 13 th Gen Intel(R) Core(TM) i9-13900K \\ GPU & NVIDIA GeForce RTX 4090 \\ System & Windows 10 \\ Framework & Pytorch 2.0.0 \\ IDE & Pycharm 2022.2.2 \\ Python version & version 3.10.9 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental setting.  
\begin{table}
\begin{tabular}{c c} \hline
**Device** & **Configuration** \\ \hline Img-size & 640 \(\times\) 640 \\ Epochs & 200 \\ Batch-size & 8 \\ Initial learning rate & 0.01 \\ Optimizer & SGD \\ \hline \end{tabular}
\end{table}
Table 2: Enhanced Model Training Parameter Setup for Forest Fire Detection.  
that the model can more accurately identify fire instances, reducing the probability of erroneously classifying non-fire instances as fires. The calculation approach is given by:  
\[Precision=\frac{TP}{TP+FP}.\]  
Assessing the model's efficacy in forest fire detection necessitates considering Recall and Precision to ensure efficient fire element detection and precise classification simultaneously.  
Average Precision (\(AP\)) emerges as a crucial performance measure, providing a comprehensive evaluation of the Precision-Recall trade-off. The formula for \(AP\) calculation is as follows:  
\[AP=\int_{0}^{1}P(r)\,dr,\]  
where the calculation of \(AP\) involves weighting and averaging the Average Precision values for different classes to obtain \(mAP\):  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i},\]  
where \(n\) represents the total number of categories, and where \(AP_{i}\) is the mean precision value for the \(i\)-th category. In this study, as forest fires belong to a single category, \(AP\) was employed as the overall performance assessment metric, with an IOU threshold set at 50%, denoted as \(AP\emptyset\)50.  
Frames per Second (FPS) represents the rate at which the model processes images during target detection, providing a measure of the model's detection speed. The interplay between accuracy and FPS can be assessed by measuring the real-time performance of the model in detection tasks, with FPS and accuracy mutually influencing the model's feasibility in practical applications.