Visible and Infrared Image Fusion Framework based on RetinaNet for Marine Environment

Farahnakian, Fahimeh; Poikonen, Jussi; Laurinen, Markus; Makris, Dimitrios; Heikkonen, Jukka

doi:10.23919/fusion43075.2019.9011182

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…RGB camera [10][11][12][13][14][15][16] Rich image information, low price. Lack of relevant datasets, constrained by image analysis technology.…”

Section: Metricsmentioning

confidence: 99%

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

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The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture.

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“…RGB camera [10][11][12][13][14][15][16] Rich image information, low price. Lack of relevant datasets, constrained by image analysis technology.…”

Section: Metricsmentioning

confidence: 99%

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Visible images offer rich color and texture information, while infrared images succeed in capturing thermal radiation data in low-light conditions. The fusion of these image modalities yields valuable insights for a wide range of applications, such as intelligent urban surveillance [ 1 ], environmental monitoring [ 2 ], autonomous vehicles [ 3 ], medical diagnostics [ 4 , 5 ], military surveillance [ 6 ], and precision weapon targeting. Researchers in this domain have diligently advanced various methods, classifiable into three main categories based on their processing techniques: multi-scale transformation, sparse representation, and deep learning [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images

Hao,

Chen,

Ning

et al. 2024

Sensors

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Latent Low-Rank Representation (LatLRR) has emerged as a prominent approach for fusing visible and infrared images. In this approach, images are decomposed into three fundamental components: the base part, salient part, and sparse part. The aim is to blend the base and salient features to reconstruct images accurately. However, existing methods often focus more on combining the base and salient parts, neglecting the importance of the sparse component, whereas we advocate for the comprehensive inclusion of all three parts generated from LatLRR image decomposition into the image fusion process, a novel proposition introduced in this study. Moreover, the effective integration of Convolutional Neural Network (CNN) technology with LatLRR remains challenging, particularly after the inclusion of sparse parts. This study utilizes fusion strategies involving weighted average, summation, VGG19, and ResNet50 in various combinations to analyze the fusion performance following the introduction of sparse parts. The research findings show a significant enhancement in fusion performance achieved through the inclusion of sparse parts in the fusion process. The suggested fusion strategy involves employing deep learning techniques for fusing both base parts and sparse parts while utilizing a summation strategy for the fusion of salient parts. The findings improve the performance of LatLRR-based methods and offer valuable insights for enhancement, leading to advancements in the field of image fusion.

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RGB and Depth Image Fusion for Object Detection Using Deep Learning

Farahnakian

Heikkonen

2021

Advances in Intelligent Systems and Computing

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Visible and Infrared Image Fusion Framework based on RetinaNet for Marine Environment

Cited by 7 publications

References 19 publications

A review of intelligent ship marine object detection based on RGB camera

A review of intelligent ship marine object detection based on RGB camera

Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images

RGB and Depth Image Fusion for Object Detection Using Deep Learning

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