UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information

Girisha, S; Verma, Ujjwal; Pai, M. M. Manohara; Pai, Radhika M.

doi:10.1109/jstars.2021.3069909

Cited by 51 publications

(16 citation statements)

References 54 publications

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“…Based on residual network, it changed the dilated convolution kernels before extracting the correlations between geophysical objects, thus improving the segmentation accuracy and used a pixel-level method to achieve semantic segmentation. Girisha et al [ 19 ] proposed an enhanced encoder–decoder based CNN architecture (UVid-Net) for unmanned aerial vehicle (UAV) video semantic segmentation. This advanced algorithm greatly enhanced the accuracy of the localization.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

Yang¹,

Guo²

2022

Computational Intelligence and Neuroscience

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With the development of deep learning theory and the decrease of the cost of acquiring massive data, the image semantic segmentation algorithm based on Convolutional Neural Networks (CNNs) is gradually replacing the conventional segmentation algorithm by its high accuracy segmentation performance. By increasing the amount of training data and stacking more convolutional layers to form Deep Convolutional Neural Networks (DCNNs), a neural network model with higher segmentation accuracy can be obtained, but it faces the problems of serious memory consumption and long latency. For some special application scenarios, such as augmented reality and mobile interaction, real-time processing cannot be performed. To improve the speed of semantic segmentation while obtaining the most accurate segmentation results as possible, this paper proposes a semantic segmentation algorithm based on lightweight convolutional neural networks. Taking the computational complexity and segmentation accuracy into account, the algorithm starts from the perspective of extracting high-level semantic features and introduces a position-attention mechanism with richer contextual information to model the relationship between different pixels, avoiding the convolutional local perceptual field to be too small. To recover clearer target boundaries, a channel attention mechanism is introduced in the decoding part of the model to mine more useful feature channel information and effectively improve the fusion of low-level features with high-level features. By verifying the effectiveness of the above model on a publicly available dataset and comparing it with the more popular semantic segmentation methods, the model proposed in this paper has higher semantic segmentation accuracy and reflects certain advantages in objective evaluation.

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Section: Related Workmentioning

confidence: 99%

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

Yang¹,

Guo²

2022

Computational Intelligence and Neuroscience

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“…Moreover, they explore the performance of semantic segmentation algorithms for aerial videos achieved with the Fully Convolution Networks (FCN) and U-net architectures. Girisha et al [119] proposed an enhanced encoder-decoderbased CNN architecture (UVid-Net) for UAV video semantic segmentation. The encoder can embed temporal information in terms of temporally consistent labelling.…”

Section: Semantic Segmentationmentioning

confidence: 99%

A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform

Han

Liu

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Unmanned Aerial Vehicles (UAVs) have been widely applied in military and civilian fields due to their flexibility and effectiveness. As a vital component of UAVs, the vision system has taken on great significance in different applications (e.g., autonomous landing, traffic surveillance, and disaster rescue) to attract widespread attention in recent years. Therefore, the automatic understanding of visual data collected from these air platforms becomes urgently needed in UAV systems. In this review, we revisit and summarize the recent techniques and developments for several typical UAV applications, including object detection, object tracking, and semantic segmentation. In addition, we also highlight the difficulties and subsequent orientations from different perspectives, which may stimulate future research and applications in the UAV vision era.

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“…In 2021, Girisha et al [69] have an improved encoderdecoder-based CNN architecture termed Uvid-Net for semantic segmentation from UAV video frames. is architecture was used to incorporate the temporal smoothness, which has captured the correlation among the sequence of frames using multibranch CNNs.…”

Section: Literature Review On State-of-the-artmentioning

confidence: 99%

Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques

Anilkumar

Venugopal

2022

Security and Communication Networks

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Semantic segmentation is a significant research topic for decades and has been employed in several applications. In recent years, semantic segmentation has been focused on different deep learning approaches in the area of computer vision, which has aimed for getting superior efficiency while analyzing the aerial and remote-sensing images. The main aim of this review is to provide a clear algorithmic categorization and analysis of the diverse contribution of semantic segmentation of aerial images and expects to give the comprehensive details associated with the recent developments. In addition, the emerged deep learning methods demonstrated much improved performance measures on several public datasets and incredible efforts have been dedicated to advancing pixel-level accuracy. Hence, the analysis on diverse datasets of each contribution is studied, and also, the best performance measures achieved by the existing semantic segmentation models are evaluated. Thus, this survey can facilitate researchers in understanding the development of semantic segmentation in a shorter time, simplify understanding of its latest advancements, research gaps, and challenges to be used as a reference for developing the new semantic image segmentation models in the future.

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UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information

Cited by 51 publications

References 54 publications

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform

Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques

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