RETRACTED ARTICLE: Intelligent video analysis for enhanced pedestrian detection by hybrid metaheuristic approach

Preethaa, K. R. Sri; Sabari, A.

doi:10.1007/s00500-020-04674-5

“…The first one is to explore the clear image without reference for training the dehazing network [45,46,51,52]. The second is to explore video dehazing [47][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

GGADN: Guided Generative Adversarial Dehazing Network

Zhang

¹

,

Song

²

2021

Preprint

0

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Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

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“…The first one is to explore the clear image without reference for training the dehazing network [45,46,51,52]. The second is to explore video dehazing [47][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

Zhang

¹

,

Qian

²

,

Song

³

2021

Soft Comput

3

0

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Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

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“…Advanced Driver Assistance (ADAS) systems are designed to improve vehicle safety and driving comfort. One of the most significant difficulties facing ADAS is the perception of the landscape and guidance of the vehicles in actual outdoor scenes including pedestrian detection [42,31,11,30], vehicle environment perception [29,50,28,13,12], traffic sign detection [15,14,16,36], and so on.…”

Section: Introductionmentioning

confidence: 99%

Automatic road sign detection and recognition based on neural network

Lahmyed

¹

,

Ansari

²

,

Kerkaou

³

2022

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Road sign detection and recognition is an integral part of intelligent transportation systems (ITS). It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using Artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns (GLPB) feature to the RGB color space and the local self-similarity (LSS) feature. ANN, Adaboost and support vector machine (SVM) have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databasets, well known in the traffic sign community (GTSRB, GTSDB and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods. KeywordsTraffic sign detection • Traffic sign recognition • Color segmentation • Artificial neural networks (ANN) • Support vector machines (SVMs) • Histogram of dominant silhouette orientation • Gradient local binary patterns (GLBP) • Local self-similarity (LSS).

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RETRACTED ARTICLE: Intelligent video analysis for enhanced pedestrian detection by hybrid metaheuristic approach

Cited by 24 publications

References 31 publications

GGADN: Guided Generative Adversarial Dehazing Network

GGADN: Guided Generative Adversarial Dehazing Network

GGADN: Guided generative adversarial dehazing network

Automatic road sign detection and recognition based on neural network

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