title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÑÐ´Ð¾Ð² Ð¿Ð¾ Ð´Ð°Ð½Ð½ÑÐ¼ Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÑÑÐ¼ÐºÐ¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
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: Detection of vessels by aerial photography using a convolutional neural network
Creators: name: ÐÐµ Ð¦Ð·Ð¸Ð½ÑÐ»ÑÐ½
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: 2019
Subjects: Ð¡ÑÐ´Ð°
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ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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ÑÐµÑÐµÐ²Ð°Ñ ÑÑÑÑÐºÑÑÑÐ° Mask R-CNN
ÑÐ»ÑÑÑÐµÐ½Ð¸Ðµ ÑÑÑÑÐºÑÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ°
ÑÐ»ÑÑÑÐµÐ½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Mask R-CNN

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description: With fast growing of shipping traffic, more ships increase the chances of infractions at open seas like illegal cargo movement, illegal fishing, illegal invasion ,piracy. Thus, the purpose of this thesis is to build a model that can detects all ships in satellite images as quickly as possible, for better combating those illegal activities or carry out humanitarian assistance on the open seas. For a country ship detection also is related to the country's economic development and national security, and ship detection can be used not only for rescue work of ships in distress, but also for combating
illegal fishing vessels, illegal dumping of garbage ships, combating smuggling of ships, and combating piracy. This thesis studies the latest convolutional neural network (Mask R-CNN and U-Net) and applies it to the detection and segmentation of ship in static satellite images, mainly to complete the following work: 1) We studied structure and working principle of convolutional neural network in object detection and the neural network like R-CNN, SSPNet, Fast R-CNN, Faster R-CNN, and Mask R-CNN, which are representative of the current object detection field, are studied
separately. Through the research and comparison of each network structure, we can intuitively understand the construction principle of each network, and thus more clearly knows the advantages of Mask R-CNN network structure in object detection. 2) We improved Mask R-CNN. The main improvements include: feature extraction network structure improvement, proposal window classifier structure improvement, and non-maximum value suppression algorithm improvement. 3) We analysis experimental results, experimental environment, and experimental data. Then explains how to preprocess the experimental data. Finally, the experimental results shows that the improved Mask R-CNN algorithm is robust to ship detection.