• Title/Summary/Keyword: semantic segmentation

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Semantc Segmentation Using Simple Arduino Module (간단한 아두이노 모듈을 이용한 Semantic Segmentation)

  • Ha, Soo-Hee;Yoo, Jae-Chern
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.01a
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    • pp.37-39
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    • 2021
  • 본 논문에서는 간단한 아두이노 모듈을 이용하여 MATLAB에서 실행되는 semantic segmentation을 조작해보았다. 기존에는 단순히 센서를 통해 감지하거나, 입력을 받아 출력하는 등의 수동적으로 아두이노 모듈을 활용하였다. 하지만 직접 아두이노와 semantic segmentation을 연결하여 semantic segmentation 결과를 조작하여, 아두이노를 인공지능과 결합하여 능동적으로 사용할 수 있게 하였다.

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Deep Multi-task Network for Simultaneous Hazy Image Semantic Segmentation and Dehazing (안개영상의 의미론적 분할 및 안개제거를 위한 심층 멀티태스크 네트워크)

  • Song, Taeyong;Jang, Hyunsung;Ha, Namkoo;Yeon, Yoonmo;Kwon, Kuyong;Sohn, Kwanghoon
    • Journal of Korea Multimedia Society
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    • v.22 no.9
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    • pp.1000-1010
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    • 2019
  • Image semantic segmentation and dehazing are key tasks in the computer vision. In recent years, researches in both tasks have achieved substantial improvements in performance with the development of Convolutional Neural Network (CNN). However, most of the previous works for semantic segmentation assume the images are captured in clear weather and show degraded performance under hazy images with low contrast and faded color. Meanwhile, dehazing aims to recover clear image given observed hazy image, which is an ill-posed problem and can be alleviated with additional information about the image. In this work, we propose a deep multi-task network for simultaneous semantic segmentation and dehazing. The proposed network takes single haze image as input and predicts dense semantic segmentation map and clear image. The visual information getting refined during the dehazing process can help the recognition task of semantic segmentation. On the other hand, semantic features obtained during the semantic segmentation process can provide cues for color priors for objects, which can help dehazing process. Experimental results demonstrate the effectiveness of the proposed multi-task approach, showing improved performance compared to the separate networks.

Object Segmentation Using ESRGAN and Semantic Soft Segmentation (ESRGAN과 Semantic Soft Segmentation을 이용한 객체 분할)

  • Dongsik Yoon;Noyoon Kwak
    • Journal of Internet of Things and Convergence
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    • v.9 no.1
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    • pp.97-104
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    • 2023
  • This paper is related to object segmentation using ESRGAN(Enhanced Super Resolution GAN) and SSS(Semantic Soft Segmentation). The segmentation performance of the object segmentation method using Mask R-CNN and SSS proposed by the research team in this paper is generally good, but the segmentation performance is poor when the size of the objects is relatively small. This paper is to solve these problems. The proposed method aims to improve segmentation performance of small objects by performing super-resolution through ESRGAN and then performing SSS when the size of an object detected through Mask R-CNN is below a certain threshold. According to the proposed method, it was confirmed that the segmentation characteristics of small-sized objects can be improved more effectively than the previous method.

A Study on Residual U-Net for Semantic Segmentation based on Deep Learning (딥러닝 기반의 Semantic Segmentation을 위한 Residual U-Net에 관한 연구)

  • Shin, Seokyong;Lee, SangHun;Han, HyunHo
    • Journal of Digital Convergence
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    • v.19 no.6
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    • pp.251-258
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    • 2021
  • In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

Image segmentation preserving semantic object contours by classified region merging (분류된 영역 병합에 의한 객체 원형을 보존하는 영상 분할)

  • 박현상;나종범
    • Proceedings of the IEEK Conference
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    • 1998.06a
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    • pp.661-664
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    • 1998
  • Since the region segmentation at high resolution contains most of viable semantic object contours in an image, the bottom-up approach for image segmentation is appropriate for the application such as MPEG-4 which needs to preserve semantic object contours. However, the conventioal region merging methods, that follow the region segmentation, have poor performance in keeping low-contrast semantic object contours. In this paper, we propose an image segmentation algorithm based on classified region merging. The algorithm pre-segments an image with a large number of small regions, and also classifies it into several classes having similar gradient characteristics. Then regions only in the same class are merged according to the boundary weakness or statisticsal similarity. The simulation result shows that the proposed image segmentation preserves semantic object contours very well even with a small number of regions.

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Performance Improvement of Object Segmentation Using ESRGAN and Semantic Soft Segmentation (ESRGAN과 Semantic Soft Segmentation을 이용한 객체 분할의 성능 개선)

  • Yoon, DongSik;Kwak, Noyoon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2020.05a
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    • pp.468-471
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    • 2020
  • 본 논문은 ESRGAN(Enhanced Super Resolution GAN)과 Semantic Soft Segmentation을 이용한 객체 분할의 성능 개선에 관한 것이다. 본 논문의 연구진이 이미 제안한 Mask R-CNN과 Semantic Soft Segmentation을 이용한 객체 분할 방법은 전반적으로 객체 분할 성능이 양호한 반면, 객체의 크기가 상대적으로 작으면 분할 성능이 저조해지는 문제점이 있었다. 본 논문은 이러한 문제점을 해결하기 위한 것으로, Mask R-CNN을 통해 검출된 객체의 크기가 일정 기준치 이하인 경우, ESRGAN을 통해 초해상화를 수행한 후, Semantic Soft Segmentation을 수행함으로써 소형 객체의 분할 성능을 개선함에 그 목적이 있다. 제안된 방법에 따르면, 기존의 방볍에 비해 크기가 작은 객체의 분할 특성을 좀 더 효과적으로 개선할 수 있음을 확인할 수 있었다.

Image Semantic Segmentation Using Improved ENet Network

  • Dong, Chaoxian
    • Journal of Information Processing Systems
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    • v.17 no.5
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    • pp.892-904
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    • 2021
  • An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

Multi-Path Feature Fusion Module for Semantic Segmentation (다중 경로 특징점 융합 기반의 의미론적 영상 분할 기법)

  • Park, Sangyong;Heo, Yong Seok
    • Journal of Korea Multimedia Society
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    • v.24 no.1
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    • pp.1-12
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    • 2021
  • In this paper, we present a new architecture for semantic segmentation. Semantic segmentation aims at a pixel-wise classification which is important to fully understand images. Previous semantic segmentation networks use features of multi-layers in the encoder to predict final results. However, they do not contain various receptive fields in the multi-layers features, which easily lead to inaccurate results for boundaries between different classes and small objects. To solve this problem, we propose a multi-path feature fusion module that allows for features of each layers to contain various receptive fields by use of a set of dilated convolutions with different dilatation rates. Various experiments demonstrate that our method outperforms previous methods in terms of mean intersection over unit (mIoU).

A Study of Real-time Semantic Segmentation Performance Improvement in Unstructured Outdoor Environment (비정형 야지환경 주행상황에서의 실시간 의미론적 영상 분할 알고리즘 성능 향상에 관한 연구)

  • Daeyoung, Kim;Seunguk, Ahn;Seung-Woo, Seo
    • Journal of the Korea Institute of Military Science and Technology
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    • v.25 no.6
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    • pp.606-616
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    • 2022
  • Semantic segmentation in autonomous driving for unstructured environments is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a deep learning framework for semantic segmentation that involves a pooled class semantic segmentation with five classes. The evaluation of the framework is carried out on two off-road driving datasets, RUGD and TAS500. The results show that our proposed method achieves high accuracy and real-time performance.

CRFNet: Context ReFinement Network used for semantic segmentation

  • Taeghyun An;Jungyu Kang;Dooseop Choi;Kyoung-Wook Min
    • ETRI Journal
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    • v.45 no.5
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    • pp.822-835
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    • 2023
  • Recent semantic segmentation frameworks usually combine low-level and high-level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder-decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large-scale, real-world datasets to demonstrate the effectiveness of our approach.