An efficient encode-decode deep learning network for lane markings instant segmentation

Mamun, Abdullah Al; Ping, Em Poh; Hossen, J.

doi:10.11591/ijece.v11i6.pp4982-4990

Cited by 3 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They provide a way to evaluate how well a clustering algorithm is able to group similar data points together and separate dissimilar data points into different clusters. Clustering validation metrics can help to compare different clustering algorithms, select the best algorithm or parameters for a given dataset, and provide insights into the structure of the data [21,22].…”

Section: Metrics Used In Evaluationmentioning

confidence: 99%

An Optimized Clustering Approach using Tree Seed Algorithm for the Brain MRI Images Segmentation

Boumediene ghaouti,

Meftah

2023

View full text Add to dashboard Cite

Clustering algorithms are widely used to segment medical images. However, these techniques are difficult to perform, especially in brain magnetic resonance images (MRI), given the complexity of the anatomical structure of brain tissue, the in-homogeneity of pixel intensity in these images, and partial volume and noise effects. This will cause the algorithm to fall into the local minima problem; for this reason, it is recommended to improve such clustering algorithms using optimization techniques to obtain better results. In this study, we have proposed a developed clustering algorithm and we optimized it using a tree seed algorithm (TSA) to segment brain MRI image. Algorithms are tested on real brain image datasets. The experimental results on simulated and real brain MRI datasets show that our proposed method has satisfactory results regarding the Davies-Bouldin index (DBI) compared to the fuzzy c-mean (FCM) algorithm.

show abstract

Section: Metrics Used In Evaluationmentioning

confidence: 99%

An Optimized Clustering Approach using Tree Seed Algorithm for the Brain MRI Images Segmentation

Boumediene ghaouti,

Meftah

2023

View full text Add to dashboard Cite

show abstract

“…Segmentation approaches such as [ 29 , 30 , 31 ] can be the best option for lane marking detection, as mentioned by Shriyash et al [ 32 ]. These approaches strictly emphasize per-pixel classification rather than focusing on particular shapes.…”

Section: Lmd Using Dnnmentioning

confidence: 99%

A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks

Mamun

Ping

Hossen

et al. 2022

Sensors

View full text Add to dashboard Cite

Lane marking recognition is one of the most crucial features for automotive vehicles as it is one of the most fundamental requirements of all the autonomy features of Advanced Driver Assistance Systems (ADAS). Researchers have recently made promising improvements in the application of Lane Marking Detection (LMD). This research article has taken the initiative to review lane marking detection, mainly using deep learning techniques. This paper initially discusses the introduction of lane marking detection approaches using deep neural networks and conventional techniques. Lane marking detection frameworks can be categorized into single-stage and two-stage architectures. This paper elaborates on the network’s architecture and the loss function for improving the performance based on the categories. The network’s architecture is divided into object detection, classification, and segmentation, and each is discussed, including their contributions and limitations. There is also a brief indication of the simplification and optimization of the network for simplifying the architecture. Additionally, comparative performance results with a visualization of the final output of five existing techniques is elaborated. Finally, this review is concluded by pointing to particular challenges in lane marking detection, such as generalization problems and computational complexity. There is also a brief future direction for solving the issues, for instance, efficient neural network, Meta, and unsupervised learning.

show abstract

“…Since past few decades, researchers are widely using deep networks for various applications [17]- [39]. Al Mamun et al [30] and Ahmed et al [36], deep network based methods are proposed to improve the accuracy of object tracking tasks in varying illumination conditions. End to end deep architectures are proposed for anomaly detection and localization in crowded scenes [17]- [19], [28], and [29].…”

Section: Introductionmentioning

confidence: 99%

Recognition of crowd abnormal activities using fusion of handcrafted and deep features

Pathade

Khambete

2022

IJEECS

View full text Add to dashboard Cite

Constant vigilance is extremely important at crowded public places where some unusual activities such as sudden dispersion or continuous gathering of people may lead to chaotic and disastrous situations. Automatic recognition of such collective activities of people is indeed an important task to ensure people safety. In this view, we propose a novel approach for automatic recognition of crowd merging and sudden dispersion events. The proposed method detects dispersion and merging using fusion of features extracted by deep networks with a novel set of optical flow based and density based handcrafted features. These proposed features are not affected by occlusion and illumination. These features complement the features extracted by deep network and their fusion improves the performance of event recognition by significant amount. The method is tested profoundly on benchmark public datasets as well as private datasets. Abnormal activity recognition data often suffers from high class imbalance. However, the proposed method could successfully recognize sudden dispersion and merging activities on such very small datasets having class imbalance. This proves the effectiveness and robustness of the proposed features. The proposed method also shows better performance than other state of the art methods based on deep networks.

show abstract

An efficient encode-decode deep learning network for lane markings instant segmentation

Cited by 3 publications

References 23 publications

An Optimized Clustering Approach using Tree Seed Algorithm for the Brain MRI Images Segmentation

An Optimized Clustering Approach using Tree Seed Algorithm for the Brain MRI Images Segmentation

A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks

Recognition of crowd abnormal activities using fusion of handcrafted and deep features

Contact Info

Product

Resources

About