Combining convolutional side-outputs for road image segmentation

Reis, Felipe A. L.; Almeida, Raquel; Kijak, Ewa; Malinowski, Simon; Guimarães, Silvio Jamil Ferzoli; Patrocínio, Zenilton Kleber G. do

doi:10.1109/ijcnn.2019.8851843

Cited by 17 publications

(10 citation statements)

References 26 publications

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“…Many scholars have carried out useful research on image segmentation [16][17][18][19][20][21][22][23][24][25][26][27]. Zhao et al [16] into the ACOR and improved the selection mechanism of the original ACOR to form an improved algorithm (CCACO) for the first time.…”

Section: Related Workmentioning

confidence: 99%

“…Ji et al [18] proposed a new architecture of feature aggregation, which is designed to deal with the problem that the information of each convolutional layer cannot be used reasonably and the shallow layer information is lost in the process of transmission. Reis et al [19] took advantage of the learned model in a deep architecture, by extracting side outputs at different layers of the network for the task of image segmentation. Parajuli et al [20] performed pixel-wise segmentation to classify each pixel as road or nonroad based on color and depth features in a larger neighborhood context and described a cost-effective, modular, deep convolution network design.…”

Section: Related Workmentioning

confidence: 99%

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The Segmentation of Road Scenes Based on Improved ESPNet Model

Jin

Han

et al. 2021

Security and Communication Networks

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Image segmentation is an important research in image processing and machine vision in which automated driving can be seen the main application scene of image segmentation algorithms. Due to the many constraints of power supply and communication in in-vehicle systems, the vast majority of current image segmentation algorithms are implemented based on the deep learning model. Despite the ultrahigh segmentation accuracy, the problem of mesh artifacts and segmentation being too severe is obvious, and the high cost, computational, and power consumption devices required are difficult to apply in real-world scenarios. It is the focus of this paper to construct a road scene segmentation model with simple structure and no need of large computing power under the premise of certain accuracy. In this paper, the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) model is introduced in detail. On this basis, an improved ESPNet model is proposed based on ESPNet. Firstly, the network structure of the ESPNet model is optimized, and then, the model is optimized by using a small amount of weakly labeled and unlabeled scene sample data. Finally, the new model is applied to video image segmentation based on dash cam. It is verified on Cityscape, PASCAL VOC 2012, and other datasets that the algorithm proposed in this paper is faster, and the amount of parameters required is less than 1% of other algorithms, so it is suitable for mobile terminals.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The Segmentation of Road Scenes Based on Improved ESPNet Model

Jin

Han

et al. 2021

Security and Communication Networks

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“…In the list, s-FCN-loc [12], SSLGAN [22], RBNet [23], DEEP-DIG [24], StixelNet II [42], MultiNet [43], DDN [44], RoadNet3 [45], Up-Conv-Poly [46] and ALO-AVG-MM [47] all use deep neural networks to detect the road areas. An average F1 value of 94.75% is achieved by TFCN + for UM, URBAN, and UMM road images.…”

Section: Comparison Resultsmentioning

confidence: 99%

A two‐scaled fully convolutional learning network for road detection

Liang

2021

IET Image Processing

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This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance.

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“…The OFA-Net model was tested on the official KITTI test server and the results were also compared with other's models, including the MixedCRF model [46], the ALO-AVG-MM model [47], the HybridCRF model [48], and the HID-LS model [49]. The results of the comparisons are given in Table 3 [50].…”

Section: Ofa-net Resultsmentioning

confidence: 99%

One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation

Zhang

Sun

et al. 2019

Applied Sciences

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Generally, most approaches using methods such as cropping, rotating, and flipping achieve more data to train models for improving the accuracy of detection and segmentation. However, due to the difficulties of labeling such data especially semantic segmentation data, those traditional data augmentation methodologies cannot help a lot when the training set is really limited. In this paper, a model named OFA-Net (One For All Network) is proposed to combine object detection and semantic segmentation tasks. Meanwhile, using a strategy called “1-N Alternation” to train the OFA-Net model, which can make a fusion of features from detection and segmentation data. The results show that object detection data can be recruited to better the segmentation accuracy performance, and furthermore, segmentation data assist a lot to enhance the confidence of predictions for object detection. Finally, the OFA-Net model is trained without traditional data augmentation methodologies and tested on the KITTI test server. The model works well on the KITTI Road Segmentation challenge and can do a good job on the object detection task.

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Combining convolutional side-outputs for road image segmentation

Cited by 17 publications

References 26 publications

The Segmentation of Road Scenes Based on Improved ESPNet Model

The Segmentation of Road Scenes Based on Improved ESPNet Model

A two‐scaled fully convolutional learning network for road detection

One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation

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