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Special Issue "Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 18154

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Special Issue Editors

Prof. Dr. Józef Lisowski

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Guest Editor

Department of Ship Automation, Faculty of Marine Electrical Engineering, Gdynia Maritime University, 83 Morska Str., 81-225 Gdynia, Poland
Interests: control engineering; static and dynamic optimization; differential game; positional game; matrix game; artificial intelligence; sensitivity of control; remote sensing; technology development; applications
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Prof. Dr. Kouzou Abdellah

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Faculty of Science and Technology, University of Djelfa, Algeria
Interests: intelligent remote sensors in industrial and agriculture applications; remote control in hybrid systems based on renewable energy sources
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Prof. Dr. Haitham Abu-Rub

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Guest Editor

Smart Grid Center Qatar, Texas A&M University at Qatar, Al Rayyan, Qatar
Interests: big data analytics and machine learning for smart grid applications; smart meters

Prof. Dr. Piotr Szymak

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Guest Editor

Faculty of Mechanical and Electrical Engineering, Polish Naval Academy, 81-127 Gdynia, Poland
Interests: remote sensing of maritime unmanned vehicles; remote sensing and artificial intelligence in autonomous control and navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Moving objects constitute a significant part of all technical objects for which the method of controlling their movement significantly affects both operating costs and the accuracy and safety of transport tasks. This applies to land, sea and air objects in terms of manned and unmanned facilities. Remote sensing devices such as radar, lidar and other highly specialized measurement solutions are used in the detection and control of moving objects. When planning and implementing motion control of objects, there are many possible acceptable solutions from which the best or optimal solution should be selected. To find it, both static and dynamic optimization deterministic methods and heuristic methods of particle swarms are used, as well as very effective methods of artificial intelligence in the form of evolutionary algorithms and neuro-fuzzy regulators.

In addition, moving objects are often affected by various types of interference, which are compensated for by adaptive algorithms using the following methods: self-tuning, model reference system or gain scheduling.

When carrying out transport tasks, there are situations of passing by many other objects. In such situations, the subjectivity of the operator of the object in assessing the navigational situation is important. They must take into account the applicable legal rules and the possibility of making a mistake and contributing to a collision situation, which can be considered as a conflict situation. Game theory, which is a branch of modern mathematics, including the theory of conflict situations and the construction and analysis of their models, comes to the rescue. Therefore, it is appropriate to treat the process of passing objects safely as a game, taking into account the cooperation or non-cooperation between objects.

For this Special Issue, we seek innovative approaches that use remote sensing and control to develop appropriate algorithms of the computer-aided maneuvering decision, calculating all possible solutions of the task and proposing one of the best ones.

We welcome review papers, case studies, computer simulations, technology developments and applications.

Prof. Józef Lisowski
Prof. Kouzou Abdellah
Prof. Haitham Abu-Rub
Prof. Piotr Szymak
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

land, sea and aerial moving objects
manned and unmanned objects
remote sensing
artificial intelligence
control engineering
multi-criteria optimization
adaptive control
game theory application
sensitivity of control

Published Papers (9 papers)

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Research

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Open AccessArticle

Visual Object Tracking for Unmanned Aerial Vehicles Based on the Template-Driven Siamese Network

and

Remote Sens. 2022, 14(7), 1584; https://doi.org/10.3390/rs14071584 - 25 Mar 2022

Cited by 2 | Viewed by 1544

Abstract

Visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due to some complex factors including viewpoint changing, background clutters and occlusion. The Siamese trackers, which offer a convenient way of formulating the visual tracking problem as a template matching process, have achieved success in recent visual tracking datasets. Unfortunately, these template match-based trackers cannot adapt well to frequent appearance change in UAV video datasets. To deal with this problem, this paper proposes a template-driven Siamese network (TDSiam), which consists of feature extraction subnetwork, feature fusion subnetwork and bounding box estimation subnetwork. Especially, a template library branch is proposed for the feature extraction subnetwork to adapt to the changeable appearance of the target. In addition, a feature aligned (FA) module is proposed as the core of feature fusion subnetwork, which can fuse information in the form of center alignment. More importantly, a method for occlusion detection is proposed to reduce the noise caused by occlusion. Experiments were conducted on two challenging benchmarks UAV123 and UAV20L, the results verified the more competitive performance of our proposed method compared to the existing algorithms. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion

and

Remote Sens. 2022, 14(2), 420; https://doi.org/10.3390/rs14020420 - 17 Jan 2022

Cited by 23 | Viewed by 2968

Abstract

As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved great success in recent years, they are not effective in small object detection and most of them cannot achieve real-time processing. Therefore, this paper proposes a single-stage small object detection network (SODNet) that integrates the specialized feature extraction and information fusion techniques. An adaptively spatial parallel convolution module (ASPConv) is proposed to alleviate the lack of spatial information for target objects and adaptively obtain the corresponding spatial information through multi-scale receptive fields, thereby improving the feature extraction ability. Additionally, a split-fusion sub-module (SF) is proposed to effectively reduce the time complexity of ASPConv. A fast multi-scale fusion module (FMF) is proposed to alleviate the insufficient fusion of both semantic and spatial information. FMF uses two fast upsampling operators to first unify the resolution of the multi-scale feature maps extracted by the network and then fuse them, thereby effectively improving the small object detection ability. Comparative experimental results prove that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets and achieves a high real-time performance. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Stereo Vision System for Vision-Based Control of Inspection-Class ROVs

Stanisław Hożyń

and

Bogdan Żak

Remote Sens. 2021, 13(24), 5075; https://doi.org/10.3390/rs13245075 - 14 Dec 2021

Cited by 3 | Viewed by 1595

Abstract

The inspection-class Remotely Operated Vehicles (ROVs) are crucial in underwater inspections. Their prime function is to allow the replacing of humans during risky subaquatic operations. These vehicles gather videos from underwater scenes that are sent online to a human operator who provides control. Furthermore, these videos are used for analysis. This demands an RGB camera operating at a close distance to the observed objects. Thus, to obtain a detailed depiction, the vehicle should move with a constant speed and a measured distance from the bottom. As very few inspection-class ROVs possess navigation systems that facilitate these requirements, this study had the objective of designing a vision-based control method to compensate for this limitation. To this end, a stereo vision system and image-feature matching and tracking techniques were employed. As these tasks are challenging in the underwater environment, we carried out analyses aimed at finding fast and reliable image-processing techniques. The analyses, through a sequence of experiments designed to test effectiveness, were carried out in a swimming pool using a VideoRay Pro 4 vehicle. The results indicate that the method under consideration enables automatic control of the vehicle, given that the image features are present in stereo-pair images as well as in consecutive frames captured by the left camera. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing

Agnieszka Lazarowska

Remote Sens. 2021, 13(16), 3265; https://doi.org/10.3390/rs13163265 - 18 Aug 2021

Cited by 10 | Viewed by 2109

Abstract

The paper presents a comparative analysis of recent collision avoidance and real-time path planning algorithms for ships. Compared methods utilize radar remote sensing for target ships detection. Different recently introduced approaches are briefly described and compared. An emphasis is put on input data reception using a radar as a remote sensing device applied in order to detect moving obstacles such as encountered ships. The most promising methods are highlighted and their advantages and limitations are discussed. Concluding remarks include proposals of further research directions in the development of collision avoidance methods utilizing radar remote sensing. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Tracking of Multiple Maneuvering Random Hypersurface Extended Objects Using High Resolution Sensors

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Remote Sens. 2021, 13(15), 2963; https://doi.org/10.3390/rs13152963 - 28 Jul 2021

Cited by 4 | Viewed by 1321

Abstract

With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Energy-Efficient Navigation of an Autonomous Swarm with Adaptive Consciousness

Jawad Naveed Yasin

Huma Mahboob

Mohammad-Hashem Haghbayan

Muhammad Mehboob Yasin

and

Juha Plosila

Remote Sens. 2021, 13(6), 1059; https://doi.org/10.3390/rs13061059 - 11 Mar 2021

Cited by 10 | Viewed by 1481

Abstract

The focus of this work is to analyze the behavior of an autonomous swarm, in which only the leader or a dedicated set of agents can take intelligent decisions with other agents just reacting to the information that is received by those dedicated agents, when the swarm comes across stationary or dynamic obstacles. An energy-aware information management algorithm is proposed to avoid over-sensation in order to optimize the sensing energy based on the amount of information obtained from the environment. The information that is needed from each agent is determined by the swarm’s self-awareness in the space domain, i.e., its self-localization characteristics. A swarm of drones as a multi-agent system is considered to be a distributed wireless sensor network that is able to share information inside the swarm and make decisions accordingly. The proposed algorithm reduces the power that is consumed by individual agents due to the use of ranging sensors for observing the environment for safe navigation. This is because only the leader or a dedicated set of agents will turn on their sensors and observe the environment, whereas other agents in the swarm will only be listening to their leader’s translated coordinates and the whereabouts of any detected obstacles w.r.t. the leader. Instead of systematically turning on the sensors to avoid potential collisions with moving obstacles, the follower agents themselves decide on when to turn on their sensors, resulting in further reduction of overall power consumption of the whole swarm. The simulation results show that the swarm maintains the desired formation and efficiently avoids collisions with encountered obstacles, based on the cross-referencing feedback between the swarm agents. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessTechnical Note

Quality Analysis of Small Maritime Target Detection by Means of Passive Radar Reflectors in Different Sea States

Józef Lisowski

and

Andrzej Szklarski

Remote Sens. 2022, 14(24), 6342; https://doi.org/10.3390/rs14246342 - 14 Dec 2022

Viewed by 697

Abstract

The paper presents the synthesis and research of a new, more relevant detection model for Small Maritime Targets SMT such as yachts, sailing ships, fishing boats, and fishing cutters. For this purpose, effective reflection surfaces of four types of passive radar reflectors were identified in a special laboratory anechoic chamber. A fluctuation model for small maritime target detection using the Weibull probability distribution was formulated. Analytical and experimental verification of the quality of the developed model was carried out by a comparative assessment of the detection probability of small maritime targets with the use of four types of reflectors for five sea wave states. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessTechnical Note

A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor

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Remote Sens. 2021, 13(9), 1796; https://doi.org/10.3390/rs13091796 - 05 May 2021

Cited by 4 | Viewed by 1797

Abstract

Collision-avoidance is a crucial research topic in robotics. Designing a collision-avoidance algorithm is still a challenging and open task, because of the requirements for navigating in unstructured and dynamic environments using limited payload and computing resources on board micro aerial vehicles. This article presents a novel depth-based collision-avoidance method for aerial robots, enabling high-speed flights in dynamic environments. First of all, a depth-based Euclidean distance field mapping algorithm is generated. Then, the proposed Euclidean distance field mapping strategy is integrated with a rapid-exploration random tree to construct a collision-avoidance system. The experimental results show that the proposed collision-avoidance algorithm has a robust performance at high flight speeds in challenging dynamic environments. The experimental results show that the proposed collision-avoidance algorithm can perform faster collision-avoidance maneuvers when compared to the state-of-art algorithms (the average computing time of the collision maneuver is 25.4 ms, while the minimum computing time is 10.4 ms). The average computing time is six times faster than one baseline algorithm. Additionally, fully autonomous flight experiments are also conducted for validating the presented collision-avoidance approach. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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Open AccessProject Report

The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

Piotr Szymak

Paweł Piskur

and

Krzysztof Naus

Remote Sens. 2020, 12(18), 3020; https://doi.org/10.3390/rs12183020 - 16 Sep 2020

Cited by 16 | Viewed by 2799

Abstract

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence)

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