title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð´Ð¾ÑÐ¾Ð¶Ð½ÑÑ Ð·Ð½Ð°ÐºÐ¾Ð² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Deep learning techniques for the traffic sign detection and recognition
Creators: name: Ð¡Ð¼Ð¾Ð»ÑÑÐ½Ð¸Ð½Ð¾Ð²Ð°, Ð¡Ð¾ÑÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐÑÐ°Ð²Ð¸Ð»Ð° Ð´Ð¾ÑÐ¾Ð¶Ð½Ð¾Ð³Ð¾ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ ÑÐµÑÐ¸
Ð¼ÐµÑÐ¾Ð´ mask r-cnn

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½ Ð¿ÑÐ¸Ð¼ÐµÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð° Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð´Ð¾ÑÐ¾Ð¶Ð½ÑÑ Ð·Ð½Ð°ÐºÐ¾Ð² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð° Mask R-CNN. ÐÐ°Ðº ÑÐ°ÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ðµ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² (Ð² ÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸, Ð´Ð¾ÑÐ¾Ð¶Ð½ÑÑ Ð·Ð½Ð°ÐºÐ¾Ð²) Ð¸ ÐµÐµ ÑÐµÑÐµÐ½Ð¸Ðµ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº ÑÐµÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ ÑÐµÑÐµÐ¹. ÐÐ¾ Ð¸ÑÐ¾Ð³Ð°Ð¼ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ñ Ðº ÑÐ¸ÑÑÐµÐ¼Ðµ, Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ñ ÐºÐ»ÑÑÐµÐ²ÑÐµ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð¸ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÑ Ð´Ð»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸. ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð´Ð¾ÑÐ¾Ð¶Ð½ÑÑ Ð·Ð½Ð°ÐºÐ¾Ð², Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð°Ñ Ð½Ð° Ð¼ÐµÑÐ¾Ð´Ðµ Mask R-CNN, Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° ÑÐ¾ÑÐ¾ÑÐ¸Ð¹ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ð´Ð»Ñ Ð¼ÐµÑÑÐ¸ÐºÐ¸ mAp Ð²Ð¾ Ð²ÑÐµÐ¼Ñ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð´Ð°Ð» Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð²ÑÐ´Ð²Ð¸Ð½ÑÑÑ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ, ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÐºÐ¾ÑÐ¾ÑÑÑ Ð¼Ð¾Ð¶ÐµÑ ÑÐ»ÑÑÑÐ¸ÑÑ ÐºÐ°ÑÐµÑÑÐ²Ð¾ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ.
description: This work presents an example of using the deep learning techniques for the traffic sign detection and recognition with the help of the Mask R-CNN method. The first part of the research considered the current state of the problem of object detection and recognition (especially, traffic signs) and the possibility of using deep learning methods for this task. The main modern approaches to solve this problem, grounded in the use of convolutional networks, were also analyzed. Based on the results of the research, the requirements for the system were formed, key metrics and tools for development were identified. The software implementation of the traffic sign detection and recognition system, relying on the Mask R-CNN method, showed a good result from the mAp metric perspective during testing. The analysis of the results made it possible to put forward proposals, the implementation of which can improve the quality of recognition.