Image recognition in UAV videos using convolutional neural networks

• Published in: IET software Vol. 14; no. 2; pp. 176 - 181
• Main Authors: Quiñonez, Yadira, Lizarraga, Carmen, Peraza, Juan, Zatarain, Oscar
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.04.2020
• Subjects: aerial mapping; autonomous aerial vehicles; convolutional neural nets; convolutional neural networks; detection performance; detection process; engineering applications; fishing sector; image recognition; image sensors; images recognition; Inception V3; learning (artificial intelligence); MobileNet V2; NASNet-A; open sea; remotely operated vehicles; rescue operations; robot vision; Special Issue: Software Engineering Applications to Solve Organisations Issues; surveillance; TensorFlow platform; UAV videos; unmanned aerial vehicles; unmanned aerial vehicles; Inception V3; detection performance; fishing sector; robot vision; images recognition; detection process; rescue operations; open sea; remotely operated vehicles; UAV videos; image sensors; autonomous aerial vehicles; engineering applications; TensorFlow platform; MobileNet V2; surveillance; convolutional neural nets; convolutional neural networks; learning (artificial intelligence); aerial mapping; NASNet-A; image recognition
• ISSN: 1751-8806; 1751-8814; 1751-8814
• DOI: 10.1049/iet-sen.2019.0045

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.