Image recognition in UAV videos using convolutional neural networks. Issue 2 (1st April 2020)

• Record Type: Journal Article
• Authors: Quiñonez, Yadira; Lizarraga, Carmen; Peraza, Juan; Zatarain, Oscar
• Is Part Of: IET software. Volume 14:Issue 2(2020)
• Page Start: 176
• Page End: 181
• Publication Date: 2020-04-01
• Subjects: learning (artificial intelligence) -- autonomous aerial vehicles -- surveillance -- image sensors -- image recognition -- remotely operated vehicles -- convolutional neural nets -- robot vision; UAV videos -- convolutional neural networks -- unmanned aerial vehicles -- rescue operations -- aerial mapping -- engineering applications -- fishing sector -- detection process -- images recognition -- open sea -- detection performance -- Inception V3 -- MobileNet V2 -- NASNet-A -- TensorFlow platform; Computer software -- Periodicals; Software engineering -- Periodicals; 005.1
• DOI: 10.1049/iet-sen.2019.0045
• Languages: English
• ISSNs: 1751-8806

Abstract

Abstract : In recent years, unmanned aerial vehicles (UAVs) have been used in different areas of applications such as rescue operations, surveillance, agriculture, aerial mapping, engineering applications and research, among others, in order to perform tasks with greater efficiency. This work focuses on the use of UAVs in the fishing sector in order to optimise the detection process of a shoal of fish. In this sense, the main idea is to perform images recognition using the images acquired through videos captured by UAV in the open sea; to achieve the objective the convolutional neural networks were used, a new dataset with different images captured through UAV videos in the open sea were taken into account, these classes correspond to dolphin, dolphin_pod, open_sea, and seabirds. The training tests were by transfer of learning using the following models: Inception V3, MobileNet V2, and NASNet‐A (large) trained on TensorFlow platform. The experimental results show the detection performance with high‐precision values in reasonable processing time. This study ends with a critical discussion of the experimental results.