A Novel Fusion Method With Thermal and RGB-D Sensor Data for Human Detection

Ozcan, Ahmet; Çetin, Ömer

doi:10.1109/access.2022.3185402

Cited by 12 publications

(3 citation statements)

References 48 publications

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“…Since the classical APF method benefits from LIDAR sensors to determine the path plan, the best option may be to fuse it with the optical sensor. For the fusion of sensors, computer vision techniques have been employed in several studies [24]. These methods can be adapted to the fusion of different sensors.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Alparslan¹,

Çetin²

2023

Intelligent Automation &Amp; Soft Computing

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Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area. One of the simplest and most efficient algorithms, the artificial potential field algorithm (APF), may provide real-time navigation in those places but fall into local minimum in some cases. To overcome this problem and to present alternative escape routes for a robot, possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm. This study utilized a proposed sensor fusion method and an improved object classification method for detecting windows, doors, and stairs in buildings and these objects were classified as valid or invalid for the path planning algorithm. The performance of the approach was evaluated in a simulated environment with a quadrotor that was equipped with camera and laser imaging detection and ranging (LIDAR) sensors to navigate through an unknown closed space and reach a desired goal point. Inclusion of crossing points allows the robot to escape from areas where it is congested. The navigation of the robot has been tested in different scenarios based on the proposed path planning algorithm and compared with other improved APF methods. The results showed that the improved APF methods and the methods reinforced with other path planning algorithms were similar in performance with the proposed method for the same goals in the same room. For the goals outside the current room, traditional APF methods were quite unsuccessful in reaching the goals. Even though improved methods were able to reach some outside targets, the proposed method gave approximately 17% better results than the most successful example in achieving targets outside the current room. The proposed method can also work in real-time to discover a building and navigate between rooms.

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Section: Related Workmentioning

confidence: 99%

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Alparslan¹,

Çetin²

2023

Intelligent Automation &Amp; Soft Computing

Self Cite

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“…Affected by varying illumination conditions, the input quality of infrared and visible images is uneven. Specifically, during daytime and nighttime or under adverse weather conditions, there exists a significant imbalance in illumination, posing challenges for a uniform fusion design for visible images under well-lit conditions and infrared images captured in low-light conditions [31]. Consequently, a fusion strategy optimized for visible images may not be suitable for infrared images captured in these challenging scenarios, leading to the loss of crucial texture details in the infrared images.…”

Section: Introductionmentioning

confidence: 99%

AFIRE: Adaptive FusionNet for Illumination-Robust Feature Extraction in Heterogeneous Imaging Environments

Yu,

Miao,

Sun

et al. 2024

Preprint

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The fusion of infrared and visible images aims to synthesize a fused image that incorporates richer information by leveraging the distinct characteristics of each modality. However, the disparate quality of input images in terms of infrared and visible light significantly impacts fusion performance. Addressing this issue, we propose a deep adaptive fusion method in this paper, termed Adaptive FusionNet for Illumination-Robust Feature Extraction (AFIRE), which involves interactive processing of two input features and dynamically adjusts fusion weights under varying illumination conditions. Specifically, we introduce a novel interactive extraction structure during the feature extraction stage for both infrared and visible light, enabling the capture of more complementary information. Additionally, we design a Deep Adaptive Fusion module to assess the quality of input features and perform weighted fusion through a channel attention mechanism. Finally, a new loss function is formulated by incorporating the entropy and median of input images to guide the training of the fusion network. Extensive experiments demonstrate that AFIRE outperforms state-of-the-art methods in preserving pixel intensity distribution and texture details. Source code is available in GitHub https://github.com/ISCLab-Bistu/AFIRE.

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“…However, due to China's large population, low per capita resource share, and the proportion of industrial water in the underground water, there is a shortage of fresh water nationwide and the water supply is increasing day by day. Therefore, it is particularly important to effectively manage the limited groundwater resources [3][4].…”

Section: Introductionmentioning

confidence: 99%

Model Construction of Water Pollution Prevention Project Based on Small Sample Learning and Data Fusion

2023

WPPCP

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At present, the problem of water pollution in China is becoming more and more serious. In view of the scarcity of water resources and deterioration of environmental quality, it is necessary to effectively control pollutants in water bodies. This paper mainly studies the causes of pollutants in small sample data through learning and experiment methods, and takes them as the precondition to connect with the actual environment. On this basis, the improvement measures based on indoor water pollution monitoring and prediction are constructed to analyze, sort out and model the above problems. The emission concentration distribution map obtained by MATLAB software combined with laboratory simulation is used to verify that the above theoretical model is feasible and effective to solve the harmful problems caused by water quality deterioration. The test results show that, based on small sample learning and data fusion technology, It has a certain effect on the water pollution prevention project and can monitor the water pollution.

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A Novel Fusion Method With Thermal and RGB-D Sensor Data for Human Detection

Cited by 12 publications

References 48 publications

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

AFIRE: Adaptive FusionNet for Illumination-Robust Feature Extraction in Heterogeneous Imaging Environments

Model Construction of Water Pollution Prevention Project Based on Small Sample Learning and Data Fusion

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