Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding

Sun, Jee-Young; Jung, Seung Won; Ko, Sung-Jea

doi:10.1109/access.2020.3001679

Cited by 10 publications

(8 citation statements)

References 53 publications

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“…The former is accomplished by RAU and the latter by EAU. While similar techniques have been used in the past to improve the output of semantic segmentation [73], [74] and object detection [75], we believe that ours is the first contribution that incorporates these ideas for a reliable extraction of building footprints in aerial and satellite imagery.…”

Section: B Attention In Segmentation Networkmentioning

confidence: 99%

Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images

Chattopadhyay

Kak

2022

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

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Despite recent advances in deep-learning-based semantic segmentation, automatic building detection from remotely sensed imagery is still a challenging problem owing to large variability in the appearance of buildings across the globe. The errors occur mostly around the boundaries of the building footprints, in shadow areas, and when detecting buildings whose exterior surfaces have reflectivity properties that are very similar to those of the surrounding regions. To overcome these problems, we propose a generative adversarial network-based segmentation framework with uncertainty attention unit and refinement module embedded in the generator. The refinement module, composed of edge and reverse-attention units, is designed to refine the predicted building map. The edge attention enhances the boundary features to estimate building boundaries with greater precision, and the reverse attention allows the network to explore the features missing in the previously estimated regions. The uncertainty attention unit assists the network in resolving uncertainties in classification. As a measure of the power of our approach, as of December 4, 2021, it ranks at the second place on DeepGlobe's public leaderboard despite the fact that main focus of our approach-refinement of the building edges-does not align exactly with the metrics used for leaderboard rankings. Our overall F 1-score on DeepGlobe's challenging dataset is 0.745. We also report improvements on the previous-best results for the challenging INRIA validation dataset for which our network achieves an overall IoU of 81.28% and an overall accuracy of 97.03%. Along the same lines, for the official INRIA test dataset, our network scores 77.86% and 96.41% in overall IoU and accuracy. We have also improved upon the previous best results on two other datasets: the WHU building dataset and the Massachusetts buildings dataset.

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Section: B Attention In Segmentation Networkmentioning

confidence: 99%

Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images

Chattopadhyay

Kak

2022

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

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“…Sun et al [71] propose the Attention Feature Complementary module in order to fuse features coming from depth and RGB branches in the architecture. Sun et al [72] introduce a refinement module containing two attention units that implement gating mechanisms for Fig. 5: ICNet architecture.…”

Section: ) Mechanisms For Feature Fusionmentioning

confidence: 99%

A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design

Manfio¹,

Osório²

2023

Preprint

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Semantic image and video segmentation stand among the most important tasks in computer vision nowadays, since they provide a complete and meaningful representation of the environment by means of a dense classification of the pixels in a given scene. Recently, Deep Learning, and more precisely Convolutional Neural Networks, have boosted semantic segmentation to a new level in terms of performance and generalization capabilities. However, designing Deep Semantic Segmentation models is a complex task, as it may involve application-dependent aspects. Particularly, when considering autonomous driving applications, the robustness-efficiency tradeoff, as well as intrinsic limitations -computational/memory bounds and data-scarcity -and constraints -real-time inference -should be taken into consideration. In this respect, the use of additional data modalities, such as depth perception for reasoning on the geometry of a scene, and temporal cues from videos to explore redundancy and consistency, are promising directions yet not explored to their full potential in the literature. In this paper, we conduct a survey on the most relevant and recent advances in Deep Semantic Segmentation in the context of vision for autonomous vehicles, from three different perspectives: efficiency-oriented model development for real-time operation, RGB-Depth data integration (RGB-D semantic segmentation), and the use of temporal information from videos in temporalaware models. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective, so that the reader can not only get started, but also be up to date in respect to recent advances in this exciting and challenging research field.

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“…Pixel classification from semantic segmentation was used for numerous applications ranging from autonomous driving [27], robotic navigation and localization [28], to scene understanding [30]. Semantic segmentation based on deep learning was also involved in UAV visual landing site detection, such as with the PSPNet architecture [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Benchmark Analysis of Semantic Segmentation Algorithms for Safe Planetary Landing Site Selection

Claudet

Tomita

2022

IEEE Access

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This paper presents an in-depth analysis of state-of-the-art semantic segmentation algorithms applied to spacecraft safe planetary landing via hazard detection and avoidance. Several architectures are trained from binary safety maps and the rich dataset of the High-Resolution Imaging Science Experiment (HiRISE) embedded on Mars Reconnaissance Orbiter for realistic purposes. The study incorporates several metrics comparisons such as recognition accuracy, computational complexity, model complexity, and inference time. The proposed performance indices and combinations are analyzed and discussed. The experiments were performed using a Raspberry Pi 4B, which is a relevant commercial-of-the-shelf microcontroller surrogate of NASA's High-Performance Spaceflight Computer (HPSC) that will thrive within the next decades in space exploration. This paper allows researchers to know what has been tested on the subject and serves as a catalog for users to pick the most relevant architecture for their own application.

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Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding

Cited by 10 publications

References 53 publications

Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images

Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images

A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design

Benchmark Analysis of Semantic Segmentation Algorithms for Safe Planetary Landing Site Selection

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