Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation

Qi, Guanqiu; Wang, Huan; Haner, Matthew; Weng, Chenjie; Chen, Sixin; Zhu, Zhiqin

doi:10.1049/trit.2018.1045

Cited by 75 publications

(39 citation statements)

References 30 publications

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“…It can learn data characteristics autonomously, establish a human-like learning mechanism by simulating the neural network of human brain, and then analyze and learn the related data, such as images and texts [34,35]. CNN as a classical deep learning model can achieve the encoding of direct mapping from source images to weight map during the training process [29,36]. Thus, both activity-level measurements and weight distribution can be achieved together in an optimal way by learning network parameters.…”

Section: Related Workmentioning

confidence: 99%

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Wang

Zheng

Wei

et al. 2020

Sensors

Self Cite

100

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Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.

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Section: Related Workmentioning

confidence: 99%

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Wang

Zheng

Wei

et al. 2020

Sensors

Self Cite

100

View full text Add to dashboard Cite

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“…. Equations (28) and (29) separately show the energy densities e Ec and e Er of particle elements in coal and rock sections within CR combined specimens:…”

Section: Fracturing Conditions and Energy Density Of Particlementioning

confidence: 99%

“…In the study, mechanical properties of CR combined bodies were tested, and its failure mechanism were analysed. Failure process of the specimens was monitored through the use of an AE monitoring system and high-speed photography [28,29]. Furthermore, a calculation model for impact energies in a CR combined body with inclinations was proposed to reveal the mechanical essence of the energy release.…”

Section: Introductionmentioning

confidence: 99%

Failure Mechanism of a Coal‐Rock Combined Body with Inclinations of Structural Planes and a Calculation Model for Impact Energy

Zhao

Gao

et al. 2019

Advances in Civil Engineering

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A coal-rock (CR) combined body can be used to simulate structures of coal and rock strata, and its impact-induced failure characteristic conforms more close to engineering practice. Exploring the mechanical properties and impact energy in a CR combined body contributes to better predictions of rock bursts in coal mines. In the study, the mechanical properties of CR combined bodies with four different inclinations (0°, 15°, 30°, and 45°) of structural planes were measured, and also their failure mechanism was analysed. Based on the theory of particle mechanics, a calculation model for impact energy in a CR combined body with inclinations was established and then verified by using monitored acoustic emission (AE) data. The test results showed that inclination affected mechanical properties and failure characteristics of the CR combined body, i.e., the larger the inclination, the lower the strength and impact energy in the CR combined body and the lower the level of damage. The proposed calculation model for impact energy revealed the mechanical essence of energy accumulation and release of a CR combined body, providing a reference for investigating rock burst in coal mines.

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“…A machine learning algorithm can be applied by using training-testing (either through supervised or through unsupervised) framework in both cases. e recent trends for image retrievals are focused on deep neural networks (DNN) that are able to generate better results at a high computational cost [21][22][23]. In this paper, we aim to provide a compressive overview of the recent research trends that are challenging in the field of CBIR and feature representation.…”

Section: Introductionmentioning

confidence: 99%

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

262

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Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

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Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation

Cited by 75 publications

References 30 publications

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Failure Mechanism of a Coal‐Rock Combined Body with Inclinations of Structural Planes and a Calculation Model for Impact Energy

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

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