Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Samma, Hussein; Suandi, Shahrel Azmin; Ismail, Nor Azman; Sulaiman, Sarina; Ping, Lee Li

doi:10.1109/access.2021.3131231

Cited by 21 publications

(7 citation statements)

References 31 publications

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“…Convolution Layer: All data touching the convolutional layer undergoes a convolutional process. The filters in the convolutional layer have length, height, and thickness according to the input data channel [39], [40]. Each filter undergoes a shift and "dot" operation between the input data and the value of the filter, as shown in fig.…”

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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“…The crack detection use case deals solely with crack classification, localization, or segmentation. The application context is usually the maintenance of public buildings, for example, pavement cracks [59,60] or concrete cracks [61,62]. In addition to detecting defects, another VI use case is to check whether a part is missing or not.…”

Section: Overview Of Visual Inspection Use Casesmentioning

confidence: 99%

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Hütten,

Alves Gomes,

Hölken

et al. 2024

ASI

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Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.

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“…As a part of the IEEE International Conference on Big Data 2020, the article [7] provides insights into the model selection, tuning method, and outcomes of the Global Road Damage Detection Challenge. Using popular PyTorch frameworks like Detectron 2 and Yolo v5, the study evaluates single and multi-stage network designs for object identification and creates a benchmark.…”

Section: Literature Survey: -mentioning

confidence: 99%

Survey Paper on Road Damage Detection and Reporting System Using Fully Connected CNN

Pagar

2024

IJSREM

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Many rural and metropolitan towns, as well as road authorities, encounter challenges in mapping surface damages resulting from numerous sources such strong rains, natural catastrophes, or other events that cause cracks and holes to emerge on the road surface. These organizations or private entities look out for solutions to implement automated methods of reporting damages on a surface of the road. The majority of the time, they lack the equipment needed to map the damage to the roadways. One of the main issues facing commuters is the numerous damaged road portions they must navigate. This causes riders to often reduce their pace, losing a great deal of time and energy and lengthening the time it takes them to reach their destinations. When driving at a faster speed and suddenly encountering a damaged section of the road, road damage can frequently be fatal. Furthermore, it is capable of identifying recurring bottlenecks, determining their cause, and suggesting remedies. The majority of the time, these traffic jams are brought on by road damage, which forces commuters to go far slower than is ideal. Keywords: Smart road damage detection, classification, Machine Learning, Image segmentation, CNN, fully connected CNNs, RDD System (Road Damage Detection System).

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Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Cited by 21 publications

References 31 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

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Survey Paper on Road Damage Detection and Reporting System Using Fully Connected CNN

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