A Comparative Study of Autonomous Object Detection Algorithms in the Maritime Environment Using a UAV Platform

Vasilopoulos, Emmanuel; Vosinakis, Georgios; Krommyda, Maria; Karagiannidis, Lazaros; Ouzounoglou, Eleftherios; Amditis, Angelos

doi:10.3390/computation10030042

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…Government environmental agencies can utilize multi-UAV systems for environmental quality monitoring, including air quality, water pollution, and ecological monitoring of wildlife protection areas. Through collaborative trajectory planning, multi-UAVs can achieve intensive monitoring of specific areas, allowing for the timely detection and addressing of environmental issues [105,106].…”

Section: Environmental Monitoring and Protectionmentioning

confidence: 99%

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Wang,

Huang,

et al. 2024

Processes

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In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology.

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Section: Environmental Monitoring and Protectionmentioning

confidence: 99%

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Wang,

Huang,

et al. 2024

Processes

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“…The rapid development and exceptional flexibility of unmanned aerial vehicle (UAV) technology have positioned it as a critical driver across various industries. UAV target detection, a crucial application in UAV technology, finds widespread use in scenarios such as traffic surveillance [1], marine environment monitoring [2], and fire detection [3], providing efficient means for real-time data collection and detection in these domains.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Feature Perception for Unmanned Aerial Vehicle Target Detection Algorithm

Liu,

Zhu,

Tao

et al. 2024

Drones

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Unmanned aerial vehicle (UAV) aerial images often present challenges such as small target sizes, high target density, varied shooting angles, and dynamic poses. Existing target detection algorithms exhibit a noticeable performance decline when confronted with UAV aerial images compared to general scenes. This paper proposes an outstanding small target detection algorithm for UAVs, named Fine-Grained Feature Perception YOLOv8s-P2 (FGFP-YOLOv8s-P2), based on YOLOv8s-P2 architecture. We specialize in improving inspection accuracy while meeting real-time inspection requirements. First, we enhance the targets’ pixel information by utilizing slice-assisted training and inference techniques, thereby reducing missed detections. Then, we propose a feature extraction module with deformable convolutions. Decoupling the learning process of offset and modulation scalar enables better adaptation to variations in the size and shape of diverse targets. In addition, we introduce a large kernel spatial pyramid pooling module. By cascading convolutions, we leverage the advantages of large kernels to flexibly adjust the model’s attention to various regions of high-level feature maps, better adapting to complex visual scenes and circumventing the cost drawbacks associated with large kernels. To match the excellent real-time detection performance of the baseline model, we propose an improved Random FasterNet Block. This block introduces randomness during convolution and captures spatial features of non-linear transformation channels, enriching feature representations and enhancing model efficiency. Extensive experiments and comprehensive evaluations on the VisDrone2019 and DOTA-v1.0 datasets demonstrate the effectiveness of FGFP-YOLOv8s-P2. This achievement provides robust technical support for efficient small target detection by UAVs in complex scenarios.

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“…This hybrid approach resulted in real world accuracy of 86–90%. In [ 11 ], UAV was used to monitor the maritime environment. The authors tested different architectures for their efficiency, accuracy, and speed.…”

Section: Introductionmentioning

confidence: 99%

Every Vessel Counts: Neural Network Based Maritime Traffic Counting System

Petković,

Vujović,

Kaštelan

et al. 2023

Sensors

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Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diverse maritime traffic in Mediterranean ports. The paper proposes a real-time vessel counting system using land-based cameras is proposed for maritime traffic monitoring in ports, such as the Port of Split, Croatia. The system consists of a YOLOv4 Convolutional Neural Network (NN), trained and validated on the new SPSCD dataset, that classifies the vessels into 12 categories. Further, the Kalman tracker with Hungarian Assignment (HA) algorithm is used as a multi-target tracker. A stability assessment is proposed to complement the tracking algorithm to reduce false positives by unwanted objects (non-vessels). The evaluation results show that the system has an average counting accuracy of 97.76% and an average processing speed of 31.78 frames per second, highlighting its speed, robustness, and effectiveness. In addition, the proposed system captured 386% more maritime traffic data than conventional AIS systems, highlighting its immense potential for supporting comprehensive maritime research.

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A Comparative Study of Autonomous Object Detection Algorithms in the Maritime Environment Using a UAV Platform

Cited by 11 publications

References 16 publications

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Fine-Grained Feature Perception for Unmanned Aerial Vehicle Target Detection Algorithm

Every Vessel Counts: Neural Network Based Maritime Traffic Counting System

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