无人机电源技术与机器视觉：对比分析及未来展望

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“无人机电源技术与机器视觉应用：比较分析和未来趋势”这篇文章深入探讨了无人机领域的关键技术，包括电源技术和机器视觉应用，并进行了详细的比较分析，同时展望了这两个领域的未来发展方向。文章首先介绍了无人机电源技术的重要性，电源是无人机运行的基础，决定了其飞行时间、负载能力和稳定性。目前，无人机电源主要包括锂离子电池、燃料电池、太阳能电池等，每种都有其优势和限制。例如，锂离子电池具有高能量密度和快速充电特性，但其寿命和安全性是需要关注的问题；燃料电池则提供更长的续航能力，但成本和复杂性较高；太阳能电池则是环保且可持续的选择，但受天气和光照条件影响较大。接着，文章转而讨论了机器视觉在无人机应用中的角色。机器视觉使无人机能够实现自主导航、目标识别、环境感知等功能，极大地扩展了其应用场景，如农业监测、建筑测绘、物流配送等。随着深度学习和计算机视觉算法的发展，机器视觉的精度和实时性不断提高，为无人机带来了更智能化的可能。比较分析部分，作者对比了不同电源技术对无人机机器视觉系统的影响，如电源效率如何影响传感器性能、视觉处理单元的能耗等。同时，探讨了机器视觉技术如何优化电源管理，比如通过智能决策减少不必要的能源消耗。文章最后展望了未来趋势。在电源技术方面，研发更高效、安全、环保的电源解决方案将是主要方向，如固态电池和新型能量存储技术。而在机器视觉方面，预期将有更多集成化的解决方案出现，结合5G、AI等先进技术，实现更高级别的自动化和智能化。 "无人机电源技术与机器视觉应用：比较分析和未来趋势"这篇文章不仅提供了当前的技术概况，还为读者展示了这两个领域的交叉创新及其潜在的未来发展路径，对于无人机行业及相关研究者来说，具有很高的参考价值。

Telecom 2023, 4 462

3. AI, DL and Computer Vision in UAV Sensing

3.1. Artiﬁcial Intelligence

The goal of artiﬁcial intelligence, a rapidly developing ﬁeld, is to enable machines to

carry out tasks that would typically require human intelligence, like those made possible

by new information and technological communication systems like the Internet of Things

(IoT). Teaching machines to mimic human behavior is the ﬁeld’s main goal. Key ideas in

AI include machine learning and deep learning, which entail the construction of multiple

artiﬁcial neural network layers. Numerous industries have advanced signiﬁcantly as a

result of these methods [19–21].

3.2. Deep Learning and Computer Vision

Computer vision is the ﬁeld of artiﬁcial intelligence that enables computer systems to

extract important information from visual inputs such as images and videos and respond

accordingly. Machine vision uses AI to allow computers to perceive, observe, and under-

stand their surroundings. Image processing is used to automate tasks that the human visual

system can perform. To work properly, computer vision requires a large amount of data, as

it analyzes these data to identify and recognize speciﬁc inputs, such as frames or images.

To instruct a computer on identifying automobiles, it requires a substantial amount of car

illustrations to enable it to acquire the ability to distinguish and discern cars, including

those that are free from any physical impairment or blemish [

]. Neural networks (NNs)

are machine learning techniques that are inﬂuenced by biological neural networks. NNs use

nodes (also known as neurons) to transmit information in the same way as human bodies

do. The most frequent type of NN in UAV technology is the convolutional neural network

(CNN). CNNs are a particular kind of NN that is designed for the exclusive purpose of

identifying images. Convolution, as the title suggests, is the technique through which the

initial image at the input of a machine vision application is altered with ﬁlters that identify

signiﬁcant image features like boundaries (Figure 2). The system shall acquire knowledge

about the screened data it identiﬁes independently and correspond it with the anticipated

result. A distinguishable instance is identifying the label of an entity in a speciﬁc picture

that serves as an input. Typically, ﬁlters consisting of 3

3 or

5 × 5 squares

are employed

to detect the orientation of the boundary or characteristic: leftward, rightward, upward,

or downward.

Deep artiﬁcial neural networks frequently acquire more efﬁcient data representations

than shallow ones. Nevertheless, incorporating many layers in a CNN can lead to the

problem of vanishing or exploding gradients, which can make optimization challenging [

In reality, the back-propagation technique is utilized to comprehend the signiﬁcance of the

ﬁlter parameters’ weights utilized in the convolution process.

Figure 2. Training process of a CNN [24].

The utilization of an activation function is a crucial step in the convolution procedure.

Within a neural network, activation functions determine how the input’s weighted sum is

transformed into an output from a node or group of nodes in a speciﬁc layer of the network.

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