# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 4 Experiments
### Implementation Details  
To provide a more intuitive and accurate evaluation of object detection algorithms and facilitate comparisons between different object detection algorithms, this paper employs several commonly used evaluation metrics for object detection networks. These metrics include precision, recall, AP (average precision), mAP (mean average precision), parameters, GFLOPs (Giga Floating-point Operations per Second), FPS (Frames Per Second), and model size. Precision, representing accuracy, evaluates the model's correctness by calculating the ratio of true positive samples (correctly predicted positive samples) to the total samples that are predicted as positive by the model. \(TP\) denotes the number of actual positive samples that are predicted as positive by the model, and \(FP\) represents the number of actual negative samples that are predicted as positive by the model. The calculation formula is as shown in Equation (5). \[Precision=\frac{TP}{TP+FP} \tag{5}\]  
Recall, also known as sensitivity or the true positive rate, refers to the proportion of correctly predicted positive samples among all actual positive samples. \(FN\) represents the number of actual positive samples that are predicted as negative by the model. The calculation formula is as shown in Equation (6). \[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\(AP\), or average precision, is a metric used to evaluate the accuracy of a model in multi-class classification problems, with values ranging from 0 to 1. It is obtained by calculating the average precision and recall at different confidence thresholds. One common method for calculating \(AP\) is by plotting the precision-recall (PR) curve, where the x-axis represents recall and the y-axis represents precision. A larger area under the PR curve indicates a better model performance, as it signifies the model's ability to maintain high precision and recall at different thresholds. The calculation formula is as shown in Equation (7). \[AP=\int_{0}^{1}P(R)dr \tag{7}\]  
\(mAP\), or mean average precision, is the average value of \(AP\) across different classes. It is calculated as the mean of \(AP\) for each class, as shown in Equation (8), where'm' represents the number of classes or labels. \[mAP=\frac{1}{m}{\sum_{i=1}^{m}AP_{i}} \tag{8}\]  
The size of \(mAP\) depends on the predefined \(IoU\) threshold, where \(IoU\) (intersection over union) is a metric that is used to measure the overlap between the predicted boxes and ground truth boxes. It is calculated by determining the ratio of the intersection area of the predicted box and the ground truth box to the union area of the two. The specific formula is shown in Equation (9):  
\[IoU=\frac{A\cap B}{A\cup B} \tag{9}\]  
where \(A\) represents the predicted box, and \(B\) represents the ground truth box. \(A\cap B\) denotes the intersection area between the predicted box and the ground truth box, and \(A\cup B\) represents the union area of the predicted box and the ground truth box. In object detection, besides the commonly mentioned evaluation parameters, there are other metrics that can also assess a model's performance. Parameters refer to the total number of parameters in the model that need to be learned, including weights and biases. The parameter count is typically used to measure the size of the model, indicating the amount of the storage space that the model occupies. In object detection, a smaller model parameter count is beneficial for deployment on resource-constrained devices, suchas mobile devices or embedded systems. GFLOPs indicate the number of floating-point operations that the model performs per second and is commonly used to measure the computational complexity of the model. GFLOPs depend on the model's architecture, the number of layers, and the resolution of input images. Lower GFLOPs suggest that the model has a relatively lower computational overhead during inference, making it more suitable for environments with limited computational resources.