Can we calculate mean recall and precision I'm evaluating the accuracy in detecting objects for my image data set using three deep learning algorithms. I have selected a sample of 30 images. To measure the accuracy, I manually count the number of objects in each image and then calculate recall and precision values for three algorithms. Following is a sample:



Finally to select the best model for my data set, can I calculate the mean Recall and mean Accuracy? For Example:
For the precision metric for example you have:

$$
 Precision = \frac{TP}{TP+FP},
$$
with TP = True Positive and FP = False Positive.

Imagine you have the following values:
Image 1: $TP = 2, FP = 3$
Image 2: $TP = 1, FP = 4$
Image 3: $TP = 3, FP = 0$  

The precision scores as you calculated will be:
Image 1: $2/5$
Image 2: $1/5$
Image 3: $1$
Your average will be: $0.533$

On the other hand if you sum them all up and then calculate the precision value you get:

$P = \frac{6}{6+7} = 0.462$

This proves that averaging the precision scores is not the same as calculating the total precision in one go.

Since what you want is to know how precise your algorithm is, independently of the precision for each image, you should sum all the TP and FP and only then calculate the precision for each model. This way you will not have a biased average. The average would give the same weight to an image with a larger number of objects as to another image which had fewer objects.