## 4 Experimental and Results
### Experimental Environments and Evaluation Metrics  
All experiments were conducted in a Linux environment using an NVIDIA GeForce RTX 4090 Ti graphics card (32 GB of video memory) (NVIDIA, Santa Clara, CA, USA),  
Figure 10: (**a**) PPE category diagram; (**b**) distribution plot of x and y coordinates.  
Figure 9: Data-enhancement diagram. (**a**) Adjusting brightness; (**b**) adjusting saturation; (**c**) adding noise; and (**d**) random panning.  
the deep learning framework PyTorch version 1.7.0, and the Python 3.8 environment. To ensure the accuracy and reproducibility of the findings, a thorough analysis was conducted during the selection and optimization of hyperparameters. The initial learning rate was set at 0.01 to balance model convergence speed and stability. A lower learning rate helps prevent premature convergence to local optima, thus enhancing efficiency and maintaining accuracy. The training duration was established at 300 epochs, allowing ample time for the model to learn complex data features. This duration is chosen based on the behavior of the loss function and validation set performance, ensuring training continues until the model stabilizes. The momentum parameter was set at 0.937 to increase the optimizer's inertia, accelerating convergence and reducing training oscillations. To prevent overfitting, a weight decay of 0.0005 was applied, helping the model learn generalizable features by minimizing the weights. This setting balances the model's complexity with the data volume, effectively managing overfitting with a smaller weight decay. Considering the limits of graphics card memory and parallel processing capabilities, the batch size was determined to be 16. Experiments showed that this batch size optimally balances efficiency and performance.  
To objectively evaluate the improved MEAG-YOLO algorithm, this study uses \(mAP\), _Precision_, \(Recall\), and \(F1\) scores as evaluation metrics.  
\[mAP=\frac{1}{6}\sum_{i=1}^{6}AP_{i} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[F1=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
Six is the total number of categories in the test dataset. For each category \(i\), the average accuracy is calculated and recorded as \(AP_{i}\). The mean average precision (\(mAP\)) is obtained from the \(AP_{i}\) of each category. Key indicators for the performance evaluation include True Positives (\(TP\)), False Positives (\(FP\)), False Negatives (\(FN\)), and True Negatives (\(TN\)). These indicators demonstrate the model's performance in classification tasks. For example, \(FP\) refers to the model misclassifying a negative sample as positive. _Precision_ assesses the proportion of correctly labeled positive samples. \(FN\) is when a positive sample is misjudged as negative. \(Recall\) measures the proportion of correctly identified real positive samples. To comprehensively evaluate efficiency and performance, this study also considers the model's total number of parameters and FLOPs. The total number of parameters indicates the complexity of a model, with more parameters usually requiring higher computing resources. FLOPs reflect the computational load of the model during inference by measuring the number of floating-point operations performed.