1 research outputs found
Book spine recognition with the use of deep neural networks
ΠΠ»ΡΠ±ΠΎΠΊΠΈΠ΅ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΠΏΠΎΠ»ΡΡΠΈΠ»ΠΈ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΠ΅ΡΠ°Ρ
Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ ΡΠ΅Ρ
, Π³Π΄Π΅ ΡΡΠ΅Π±ΡΠ΅ΡΡΡ ΡΠ°Π±ΠΎΡΠ° Ρ Π±ΠΎΠ»ΡΡΠΈΠΌ ΠΎΠ±ΡΠ΅ΠΌΠΎΠΌ Π΄Π°Π½Π½ΡΡ
, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΠΎΠΊΡΡΠΆΠ°ΡΡΠ΅Π³ΠΎ ΠΌΠΈΡΠ°. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΎ ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΡΠ²Π΅ΡΡΠΎΡΠ½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ YOLO ΠΏΠΎ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ½ΠΈΠ³ Π² ΡΠ΅ΠΆΠΈΠΌΠ΅ ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ. ΠΠΏΠΈΡΠ°Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π½Π°Π±ΠΎΡΠ° Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ Π½Π° Π½Π΅ΠΌ Π³Π»ΡΠ±ΠΎΠΊΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Π° ΡΡΡΡΠΊΡΡΡΠ° ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ, ΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠ΅ ΠΌΠ΅ΡΡΠΈΠΊΠΈ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π΅Π΅ ΡΠ°Π±ΠΎΡΡ. Π’Π°ΠΊΠΆΠ΅ ΡΠ΄Π΅Π»Π°Π½ ΠΊΡΠ°ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
Π²ΠΈΠ΄ΠΎΠ² Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ. ΠΡΠ±ΡΠ°Π½Π½Π°Ρ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΎΡΠ½ΠΎΠ²Ρ Π΄Π»Ρ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠΈ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ° ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΡΡΠ΄ΠΎΠΌ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ², ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΡ
Π΅ΠΉ Π² Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΌΠ΅ΡΠ΅ ΠΊΠΎΠ½ΠΊΡΡΠΈΡΠΎΠ²Π°ΡΡ Ρ Π΄ΡΡΠ³ΠΈΠΌΠΈ ΠΌΠΎΠ΄Π΅Π»ΡΠΌΠΈ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅ΠΉ ΠΈ Π΄Π΅Π»Π°ΡΡΠΈΡ
Π΅Π΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΡΡΠΈΠΌ Π²Π°ΡΠΈΠ°Π½ΡΠΎΠΌ Π΄Π»Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠ΅ΡΠΈ, Π½Π°ΡΠ΅Π»Π΅Π½Π½ΠΎΠΉ Π½Π° Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΏΡΠΈ Π΅Π΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ Π±ΡΠ»ΠΈ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ½ΠΈΠ²Π΅Π»ΠΈΡΠΎΠ²Π°Π½Ρ Π½Π΅ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°ΡΡΠΈΠ΅ΡΡ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΈ ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ (ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ΠΌ ΡΡ
ΠΎΠΆΠΈΡ
ΠΏΠΎ ΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡ, ΠΈΠΌΠ΅ΡΡΠΈΡ
ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²ΡΠΉ ΡΠ²Π΅Ρ ΠΎΠ±Π»ΠΎΠΆΠ΅ΠΊ ΠΈΠ»ΠΈ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Π½ΡΡ
ΠΏΠΎΠ΄ Π½Π°ΠΊΠ»ΠΎΠ½ΠΎΠΌ ΠΊΠ½ΠΈΠ³). Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Π² Ρ
ΠΎΠ΄Π΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π³Π»ΡΠ±ΠΎΠΊΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π΅Π΅ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΎΡΠ½ΠΎΠ²Ρ Π΄Π»Ρ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΉ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ, ΡΠ΅Π»ΡΡ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ Π±ΡΠ΄Π΅Ρ ΡΠ²Π»ΡΡΡΡΡ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ½ΠΈΠ³ ΠΏΠΎ ΠΊΠ½ΠΈΠΆΠ½ΡΠΌ ΠΊΠΎΡΠ΅ΡΠΊΠ°ΠΌ.
Nowadays deep neural networks play a significant part in various fields of human activity. Especially they benefit spheres dealing with large amounts of data and lengthy operations on obtaining and processing information from the visual environment. This article deals with the development of a convolutional neural network based on the YOLO architecture, intended for real-time book recognition. The creation of an original data set and the training of the deep neural network are described. The structure of the neural network obtained is presented and the most frequently used metrics for estimating the quality of the network performance are considered. A brief review of the existing types of neural network architectures is also made. YOLO architecture possesses a number of advantages that allow it to successfully compete with other models and make it the most suitable variant for creating an object detection network since it enables some of the common disadvantages of such networks to be significantly mitigated (such as recognition of similarly looking, same-color book coves or slanted books). The results obtained in the course of training the deep neural network allow us to use it as a basis for the development of the software for book spine recognition