# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 4 Experiments and Results
### Performance Indicators  
In this paper, commonly used metrics in deep learning, such as Precision, Recall, Average Precision (AP), and Mean Average Precision (mAP), are adopted as core evaluation indicators, which comprehensively assess the detection performance of the network. In addition, to verify the applicability of the improved network on the UAV platform, the total number of model parameters (Params) and the number of floating-point operations per second (FLOPs), which reflect computational complexity, are also critical factors for consideration.  
Precision refers to the ratio of correctly detected objects to the total number of detected objects. This metric measures the algorithm's ability to avoid false positives. The formula for calculating Precision is  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
In Equation (3), _TP_ (True Positives) is the number of correctly detected positive class samples, and _FP_ (False Positives) is the number of negative class samples incorrectly labeled as positive.  
Recall refers to the ratio of correctly detected objects to the total number of actual objects. This metric measures the algorithm's ability to detect all relevant objects. The formula for calculating Recall is  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
In Equation (4), _FN_ (False Negatives) is the number of undetected positive class samples.  
\(F1\)-score is a metric that combines Precision and Recall into a single value, offering a balanced measure of a model's performance. It is beneficial in scenarios with a trade-off between Precision (avoiding false positives) and Recall (minimizing false negatives). The formula for calculating \(F1\)-score is as follows:  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{5}\]Average Precision (_AP_) is calculated for each object class and represents the area under the Precision-Recall curve. It is one of the most commonly used performance evaluation metrics in object detection, as it considers both precision and recall. The formula for Average Precision (_AP_) is  
\[AP=\int_{0}^{1}p(r)d(r) \tag{6}\]  
In Equation (6), \(p(r)\) is the Precision corresponding to the Recall rate \(r\).  
Mean Average Precision (_mAP_) is the average of the _AP_ results across all categories. In multi-class object-detection tasks, _mAP_ is an important metric for evaluating the algorithm's overall performance. The formula for calculating Mean Average Precision (_mAP_) is  
\[mAP=\frac{1}{N}{\sum_{i=1}^{N}Api} \tag{7}\]  
In Equation (7), \(N\) is the total number of categories, and _Api_ is the average precision of the ith category.  
The number of parameters (Params) usually refers to the number of learnable parameters in the algorithm, which includes the total number of weights and biases. The number of parameters is directly related to the complexity of the algorithm and its storage requirements and is typically used to measure the algorithm's size and computational resource consumption.  
Floating-point operations per second (FLOPs) are primarily used to describe the number of floating-point operations performed during a task's execution. They are commonly used to evaluate the computational complexity of machine learning models or high-performance computing tasks. In deep learning, an algorithm's FLOPs can help understand its scale and efficiency. Higher FLOPs indicate greater computational demands and higher energy consumption.