Purpose

The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft.

Design/methodology/approach

First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed.

Findings

The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%.

Originality/value

Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.

中文翻译：

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使用深度学习的小型飞机检测

目的

本文旨在解决基于深度学习的跟踪算法和开发的四种深度学习跟踪模型。他们相互比较以防止碰撞并在自主飞机中获得目标跟踪。

设计/方法/方法

首先，为了跟踪视觉目标，使用检测方法，然后检查跟踪方法。在这里，四个模型（深度卷积神经网络（DCNN）、带有微调的深度卷积神经网络（DCNNFN）、带有深度卷积神经网络的迁移学习（TLDCNN）和带有迁移学习的微调深度卷积神经网络（FNDCNNTL））被开发。

发现

DCNN 的训练时间为 9 分 33 秒，准确率计算为 84%。在 DCNNFN 中，网络的训练时间计算为 4 min 26 s，准确率为 91%。TLDCNN) 的训练耗时 34 分 49 秒，准确率计算为 95%。使用 FNDCNNTL，计算网络的训练时间为 34 分 33 秒，准确率接近 100%。

原创性/价值

与文献中 89.4% 到 95.6% 的结果相比，使用 FNDCNNTL，在论文中发现了更好的结果。