*3.4. Evaluation Indices*

In this paper, we use mean average precision (*mAP*) as the evaluation index. The larger the *mAP*, the higher the network detection accuracy. To calculate *mAP*, we need to calculate recall and precision first. The recall and precision can be calculated as

$$Recall = \frac{TP}{TP + FN} \tag{14}$$

$$Precision = \frac{TP}{TP + FP} \tag{15}$$

where *TP* represents the number of true positives, *FN* represents the number of false negatives, *FP* represents the number of false positives. Then, we can obtain the precision– recall curve and calculate *mAP*.

$$AP = \int\_0^1 P(R)dR\tag{16}$$

$$mAP = \frac{1}{k} \sum\_{i=1}^{k} AP\_i \tag{17}$$

where *R* represents recall, and *P* represents precision. *P*(*R*) represents the precision–recall curve.
