# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 4 Experimental
### Evaluation Indicators  
In order to test the detection performance of our proposed improved model with evaluation metrics, we use precision, recall, [EMAIL_ADDRESS], [EMAIL_ADDRESS]-0.95, parametric quantity, GFLOPs, and FPS evaluation metrics.  
Let us start with some concepts. True positive (TP) refers to the number of samples correctly predicted to be in the positive category, and false positive (FP) is the number of samples incorrectly predicted to be in the positive category. True negative (TN) refers to the number of samples correctly predicted to be in the negative category, and false negative (FN) refers to the number of samples incorrectly predicted to be in the negative category.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Set Up** \\ \hline Epochs & 300 \\ Optimizer & SGD \\ Learning rate & 0.001 \\ Momentum & 0.937 \\ weight\_decay & 0.0005 \\ batch & 32 \\ imgsz & 640 \\ iou & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Some key parameters set during the model training process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Environment** & **Configure** \\ \hline GPUs & GeForce RTX 4090 \\ CPU & Intel Core i5-10400F (2.9 GHz) \\ Systems & Windows 11 Professional \\ Framework (deep learning framework) & Torch-2.0.1 \\ Programming language versions & Python-3.8.16 \\ CUDA version (GPU parallel computing platform) & CUDA 11.7 \\ cuDNN version (deep learning GPU acceleration library) & cuDNN 8.5.0 \\ Compilers & pycharm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the training environment and hardware platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Depth** & **Width** & **Max Channels** \\ \hline YOLOv8n & 0.33 & 0.25 & 1024 \\ YOLOv8s & 0.33 & 0.50 & 1024 \\ YOLOv8m & 0.67 & 0.75 & 768 \\ YOLOv8l & 1.00 & 1.00 & 512 \\ YOLOv8x & 1.00 & 1.25 & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters corresponding to different sizes of YOLOv8.  
Precision (precision) measures the proportion of samples predicted by the model to be in the positive category that is actually in the positive category and is calculated as shown in Equation (8):  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
Recall (recall) describes the proportion of samples correctly predicted by the model to be positively classified as a percentage of all actual positively classified samples. Recall is calculated as shown in Equation (9):  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
The average precision (AP) is equal to the area under the precision-recall curve, which is calculated as shown in Equation (10):  
\[\text{AP}=\int_{0}^{1}\text{Precision}(\text{Recall})\text{d}(\text{Recall}) \tag{10}\]  
The mean average precision (mAP) is the result obtained from the weighted average of the AP values of all sample categories. It is used to measure the detection performance of the model in all categories with the formula shown in Equation (11):  
\[\text{mAP}=\frac{1}{\text{N}}\underset{i=1}{\overset{\text{N}}{\sum}}\text{ AP}_{i} \tag{11}\]  
IoU (intersection over union) denotes the ratio of intersection and concatenation of the predicted bounding box with the real bounding box. The formula is shown in Equation (12):  
\[\text{L}_{\text{IoU}}=1-\text{IoU}=1-\frac{\left|\text{A}\cap\text{B}\right|} {\left|\text{A}\cup\text{B}\right|} \tag{12}\]  
The [EMAIL_ADDRESS] metric focuses on cases where the intersection over union (IoU) equals 0.5. This means this prediction is only considered correct if the overlap between the prediction box and the real box is at least 50%.  
The [EMAIL_ADDRESS]:0.95 is a range of IoU thresholds that were considered, ranging from 0.5 to 0.95 and increasing in steps of 0.05. Here, the AP (average precision) was calculated for each threshold, and then the average of these APs was taken as the final mAP value.