TMPA-2021: Investigation of the capabilities of artificial neural networks in the problem of classifying objects with dynamic features

Nikita Laptev, Vladislav Laptev, Gerget Olga, Dmitrii Kolpashchikov and Andrey Kravchenko

Investigation of the capabilities of artificial neural networks in the problem of classifying objects with dynamic features.

TMPA is an annual International Conference on Software Testing, Machine Learning and Complex Process Analysis. The conference will focus on the application of modern methods of data science to the analysis of software quality.

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Transcript

1. Speaker: Dmitrii Y. Kolpashchikov Investigation of the capabilities of artificial neural networks in the problem of classifying objects with dynamic features
2. 1 1.1 Billion Ha 20 000 Average number of fires per year 2 Million Ha 5 Billion rub. The amount of damage to forestry Relevance Average area of forest fires per year Forest area of the Russian Federation
3. 2 It is important to the early detect fire source, precisely localize them and take timely measures to extinguish them. Timely detection and appropriate action are crucial to prevent disasters, which can save lives and property. Patrolling Satellite monitoring Aerial monitoring Video monitoring Monitoring types
4. Low flying clouds No constant shape and color Data quality is dependent on the distance between the camera and fire source, times of the day and weather conditions Detection problems 3
5. Video classification Selecting an area of smoke Localization of the source of fire Monitoring system tasks 4
6. Algorithm of the system 5 Video Algorithm for extracting dynamic features Object detection LST M Fire source localization algorithm
7. Frame preprocessing 6
8. Smoke cloud detection 7
9. Classification 9
10. Classification 9 RNN CNN GRU Network LSTM Network Fully connected neural networks Accuracy,% 74,8 77,3 69,3 Processing time, s 0,19 0,18 0,06
11. 9 Results
12. Results 9 Number of processing frames Frames size Accuracy, % Time, s 3 68,98 0,04 72,12 0,07 75,05 0,11 77,33 0,21 5 79,97 0,06 83,21 0,08 85,4 0,12 85,7 0,25 7 73,74 0,18 75,32 0,30 78,5 0,41 77,79 0,75 11 65,74 0,27 68,54 0,49 70,66 0,98 71,03 1,12 TP FP TN FN Precision Recall Accuracy 238 31 42 15 0,885 0,94 0,854
13. 9 Results
14. Thank you for your attention Team: Laptev N.V., Laptev V.V., Gerget O.M., Kolpashchikov. D.Y., Kravchenko А.А.,