RETRACTED ARTICLE: Image object detection and semantic segmentation based on convolutional neural network

Zhang, Laigang; Zhou, Sheng; Li, Yibin; Sun, Qun; Zhao, Ying; Feng, Deying

doi:10.1007/s00521-019-04491-4

Cited by 29 publications

(12 citation statements)

References 25 publications

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“…Pixel accuracy, defined as the ratio of the number of correctly classified pixels to the total number of image pixels, is the simplest metric, which measures the overall performance of pixel-level image segmentation algorithms in semantic segmentation [36].…”

Section: Image Semantic Segmentationmentioning

confidence: 99%

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Wang

Hawbani

et al. 2021

Mathematical Problems in Engineering

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With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.

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Section: Image Semantic Segmentationmentioning

confidence: 99%

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Wang

Hawbani

et al. 2021

Mathematical Problems in Engineering

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“…Literature [18] uses the convolution 4-3 layer and convolution 5-3 layer of two VGG networks to calculate the final response graph. Literature [19] uses the last layer of the network to generate a heat map for target tracking. Literature [20] uses a pretrained fully convolutional twin network to calculate sample objects and search boxes through each frame of convolution to obtain the final response map.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

Jia-dong

2021

Computational Intelligence and Neuroscience

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With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accuracy and poor robustness of the current Generic Object Tracking Using Regression Network (GOTURN) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking algorithm based on residual attention mechanism and fusion of spatiotemporal context information for data fusion. The algorithm transmits the target template, prediction area, and search area to the network at the same time to extract the general feature map and predicts the location of the tracking target in the current frame through the fully connected layer. At the same time, the residual attention mechanism network is added to the target template network structure to enhance the feature expression ability of the network and improve the overall performance of the algorithm. A large number of experiments conducted on the current mainstream target-tracking test data set show that the tracking algorithm we proposed has significantly improved the overall performance of the original tracking algorithm.

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“…Among them, image semantic segmentation is to segment different types of objects in a picture with semantic information annotation. The goal is to segment the scene image into different image areas related to semantic categories including roads, grasslands, sky and other backgrounds and discrete objects such as people, buildings, and cars [2]. This means that semantic segmentation tasks need to correctly identify different discrete objects and mark semantic information in a com-plex and changeable background.…”

Section: Introductionmentioning

confidence: 99%

“…Semantic segmentation is a typical computer vision problem. It mainly takes some raw data such as planar images and three‐dimensional point clouds as input and transforms it into a mask that highlights features through a series of technical processing [2]. Among them, image semantic segmentation is to segment different types of objects in a picture with semantic information annotation.…”

Section: Introductionmentioning

confidence: 99%

Image semantic segmentation method based on GAN network and ERFNet model

Dong¹

2021

J. eng.

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This article addresses the problems of traditional methods in image semantic segmentation, such as insufficient segmentation of small‐scale targets and weak anti‐noise ability. A method of image semantic segmentation using a generative adversarial network (GAN) combined with ERFNet model is proposed. First, the asymmetric residual module (ARM) and weak bottleneck module are used to improve the ERFNet network model. Moreover, dilated convolution is used to reduce information loss and improve the performance of small target image semantic segmentation. Then, a U‐shaped network is used to improve the generator of GAN to avoid low‐level information sharing. In addition, the residual module is introduced into convolution layer to realise the dynamic adjustment of generator weight. Finally, the improved ERFNet model is used as the input of the generator to output the segmented image. It is input to the discriminator together with the label for judgment, which further improves the performance of image semantic segmentation. The proposed method is demonstrated experimentally based on the PyTorch platform. The results show that mean pixel accuracy and mean intersection over the union of the proposed method on the CamVid and Cityscapes datasets are higher than those of other comparisons. In addition, the execution time is short, and the overall image semantic segmentation performance is relatively ideal.

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RETRACTED ARTICLE: Image object detection and semantic segmentation based on convolutional neural network

Cited by 29 publications

References 25 publications

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

Image semantic segmentation method based on GAN network and ERFNet model

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