Hybrid Image Improving and CNN (HIICNN) Stacking Ensemble Method for Traffic Sign Recognition

Yıldız, Gülcan; Ulu, Ahmet; Dіzdaroğlu, Bekіr; Yıldız, Doğan

doi:10.1109/access.2023.3292955

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…Preprocessing: The preprocessing stage aims to produce a better image to be processed later [37], [38]. That is by resizing the input image with a size of 4032 × 3024 pixels to a length of 224 × 224 pixels so that the classification process does not take a long time.…”

Section: )mentioning

confidence: 99%

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Ariessaputra,

Vidiasari,

Sasongko

et al. 2024

JOIV : Int. J. Inform. Visualization

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Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.

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Section: )mentioning

confidence: 99%

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Ariessaputra,

Vidiasari,

Sasongko

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Arcos et al arcos2018deep present a CNN architecture achieving 99.71% accuracy, with consistent precision, recall, and F1 scores all at 99.71%. Yildiz et al 54 propose an Ensemble model achieving 99.75% accuracy and high precision, recall, and F1 scores (99.75% and 99.76%). In this context, our CNN-based method stands out with an accuracy of 99.86%, precision of 99.83%, recall of 99.85%, and F1 score of 99.84%, showcasing both competitive accuracy and remarkable precision-recall performance.…”

Section: Performance Accuracy With Gtsrbmentioning

confidence: 99%

“…In comparison, Aghdam et al 53 utilizes a ConvNet, achieving 99.61% accuracy and notable precision, recall, and F1 score values of 99.37%, 99.63%, and 99.40%, respectively, with 5.6 million parameters. Similarly, Arcos et al 24 adopts a CNN with remarkable across-the-board metrics of 99.71%, while Yildiz et al 54 leverages an ensemble method, obtaining 99.75% accuracy, 99.76% recall, and 99.75% F1 score, using 14.34 million parameters. Remarkably, our proposed CNN-based model achieves the highest performance, boasting an accuracy of 99.86%, precision of 99.83%, recall of 99.85%, and an outstanding F1 score of 99.84%, all while maintaining a significantly lower parameter count of 4.6 million.…”

Section: Performance Accuracy With Btscmentioning

confidence: 99%

BanTrafficNet: Bangladeshi Traffic Sign Recognition Using A Lightweight Deep Learning Approach

Tusher,

Farid,

Kafi

et al. 2024

Preprint

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Traffic sign recognition is a critical aspect of intelligent transportation systems that enhances road safety, traffic management, and driver assistance. Notably, while significant research efforts have been devoted to many prevalent traffic signs, Bangladeshi sign recognition remains relatively underexplored due to a lack of traffic sign knowledge and the unawareness of the sector individuals. The scarcity of Bangladeshi traffic sign datasets has exacerbated this limitation, hindering progress. Moreover, most Bangladeshi traffic sign research predominantly relies on a limited number of signs, leading to diminished accuracy and the inability to detect all traffic signs. Additionally, existing systems grapple with suboptimal performance accuracy and heightened computational complexity, further emphasizing the research gap. To address these formidable challenges, we propose a robust Bangladeshi traffic sign recognition system using a deep learning-based CNN architecture. The main aim is to create a lightweight model that helps identify traffic signs correctly with less computational effort. We used the effective combination of various deep learning layers in the proposed system. A significant contribution of our work lies in creating a novel Bangladesh Traffic Sign Recognition (BDTSR) dataset, addressing the scarcity of data in this domain. This dataset comprises comprehensive information, including 48 types of traffic signs, and these images were taken from different roads in the country. Our dataset aims to fill a critical gap in Bangladeshi traffic sign research and provides a solid foundation for more extensive and inclusive studies in the field. Through this innovative approach, we aim to contribute significantly to the field of BDTSR, filling the gaps in traffic sign recognition and bolstering the accessibility of traffic signs within the Bangladeshi individual traffic community and beyond. Our evaluation of the newly created BDTSR dataset and the existing two benchmark datasets resulted in high recognition accuracies of 99.34\%, 96.97\% and 98.82\% accuracies for the BDTSR, German Traffic Sign Recognition Benchmark (GTSRB) and the Belgian Traffic Sign Dataset (BTSC) respectively. These resultshighlight the superiority of our model in the Bangladeshi Traffic Sign Recognition domain, outperforming existing models in terms of accuracy and computational efficiency.

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“…Several studies on tools made of different materials, but subjected to the same operating conditions, were grouped into a singular category, showing significant findings [48][49][50][51]. In [48], five types of solar panels were used to collect data on different types of contamination.…”

Section: A Test Objects Main Goals and Data Collectionmentioning

confidence: 99%

“…In [48], five types of solar panels were used to collect data on different types of contamination. Similarly, a stacked ensemble learning technique was employed to explore traffic sign recognition [49], malware detection [50], and sand-dust storm forecasting [50]. These examples allowed us to streamline this method while still capturing the impact of different insulator materials under the same weather conditions.…”

Section: A Test Objects Main Goals and Data Collectionmentioning

confidence: 99%

Stacked Ensemble Deep Learning for Outdoor Insulator Surface Condition Classification: A Profound Study on Water Droplets

Serikbay,

Bagheri,

Zollanvari

2023

IEEE Access

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Insulators are vital protection and isolation barriers used in power transmission systems. To prevent unexpected failures caused by severe weather conditions, it is important to develop intelligent insulator fault prognosis systems. To this end, this study has focused to examine and analyze normal (clean) and contaminated (wet) insulators through a new technique that can act as a catalyst for possible future solutions to the data-driven classification of insulator surface conditions. In particular, a novel stacked ensemble learning based on six pretrained deep convolutional neural networks (CNNs), which are used as level-0 generalizers in the stacked structure, is proposed. As the level-1 generalizer that is used in the stacked structure, a majority vote is considered among selected subsets of level-0 generalizers. In other words, training the level-1 generalizer is equivalent to selecting the best subset of pretrained CNNs for classifying a clean insulator surface from those sprayed with water droplets. Since a wetted insulator surface can enhance the electric field intensity and may lead to flashover when combined with other contaminants (dust, soil, cement, etc.), water droplets are considered a type of contamination in our study. Considering a tradeoff between performance and model complexity in training the level-1 generalizer points to two combinations of pretrained CNNs, namely, EfficientNetB2-ResNet50-Xception, and MobileNet-DenseNet121-Xception. Our empirical results show that (i) both combinations lead to better performance when compared with individual pretrained models, and (ii) the latter combination leads to a considerably lower complexity (~39% less parameters) at the expense of ~9% reduction in accuracy.INDEX TERMS deep learning, high-voltage insulators, pretrained CNN architectures, stacked ensemble learning.

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Hybrid Image Improving and CNN (HIICNN) Stacking Ensemble Method for Traffic Sign Recognition

Cited by 5 publications

References 32 publications

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

BanTrafficNet: Bangladeshi Traffic Sign Recognition Using A Lightweight Deep Learning Approach

Stacked Ensemble Deep Learning for Outdoor Insulator Surface Condition Classification: A Profound Study on Water Droplets

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