Page 88 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)

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2.3 Measurements

In this paper, after experimentally training the model, we used measurements to
compare, evaluate, specifically:

MAP (Mean Average Precision): This is a measure of the aggregate results of many
queries applied to the search system. To calculate, we must have AP (Average
Precision) as the average of the precisions at the threshold points returned by each
correct result, written with the following formula:

with recalls(n) = Rs(n) = 0, precisions(n) = Ps(n) = 1, n = threshold coefficient.
Once AP is available, the formula for mAP is written as follows:

AP k = AP value of class k, n = number of layers

F1 curve: is a tool commonly used in classification tasks to evaluate the performance
of a model with the formula:
(3)

Table 3. Measurements
Measurability Meaning Formula
mAP Average accuracy measurement
Precision Prediction accuracy TP/ (TP+FP)
Recall The goodness of correct predictability TP/(TP+FN)

Training time Training period
Memory Memory used

Where, TP is the rate of true positives, TN is the rate of true negatives, FP is the rate of
false positives, and FN is the rate of false negatives.

2.4 Results

Object Detection has color image input and output is object and position of objects in
the image. Real-time object detection with high accuracy is a requirement for the
problem of identifying hot weapons, specifically in this article is Pistol (pistol)
identification. During training, if the error level (Loss) of the current loop and the
average error of the model (Model Avg Loss) change little, stop the training process.
Testing result was shown on some images containing pistol (pistols) and showed that
the objects detected in the images were rectangles with red borders, the results of the

CITA 2023 ISBN: 978-604-80-8083-9