Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks

Dewi, Christine; Chen, Rung-Ching; Liu, Yanting

doi:10.1142/s2196888822500191

Cited by 8 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, a collection of labeled images embodying the pests of interest is compiled, with annotations encompassing both the presence and absence of pests, and, where pertinent, the exact species. This dataset is anticipated to be representative, encompassing various life stages and environmental contexts [29,30]. A model with prior training on a comprehensive dataset, such as ResNet 50 or ResNet101 utilizing the ImageNet database, is selected.…”

Section: Proposed Methodologymentioning

confidence: 99%

Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet

Dewi,

Christanto,

Dai

2023

ATAIML

View full text Add to dashboard Cite

In the realm of agriculture, crop yields of fundamental cereals such as rice, wheat, maize, soybeans, and sugarcane are adversely impacted by insect pest invasions, leading to significant reductions in agricultural output. Traditional manual identification of these pests is labor-intensive and time-consuming, underscoring the necessity for an automated early detection and classification system. Recent advancements in machine learning, particularly deep learning, have provided robust methodologies for the classification and detection of a diverse array of insect infestations in crop fields. However, inaccuracies in pest classification could inadvertently precipitate the use of inappropriate pesticides, further endangering both agricultural yields and the surrounding ecosystems. In light of this, the efficacy of nine distinct pre-trained deep learning algorithms was evaluated to discern their capability in the accurate detection and classification of insect pests. This assessment utilized two prevalent datasets, comprising ten pest classes of varied sizes. Among the transfer learning techniques scrutinized, adaptations of ResNet-50 and ResNet-101 were deployed. It was observed that ResNet-50, when employed in a transfer learning paradigm, achieved an exemplary classification accuracy of 99.40% in the detection of agricultural pests. Such a high level of precision represents a significant advancement in the field of precision agriculture.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet

Dewi,

Christanto,

Dai

2023

ATAIML

View full text Add to dashboard Cite

show abstract

“…Many hand recognition algorithms in the past depended on skin color segmentation to distinguish and extract hands from the backdrop of images, which was inefficient. In their papers [16,17], Dardas et al suggested a thresholding technique for fragmenting hands in the hue, saturation, and value (HSV) color space after extracting other skin regions, such as the face, from the source image. Girondel and colleagues [18] experimented with a variety of color spaces and discovered that the Cb and Cr channels in the YCbCr color space performed well in the skin detection task.…”

Section: Hand Detection and Recognitionmentioning

confidence: 99%

Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection

Dewi

Christanto

2022

BDCC

Self Cite

View full text Add to dashboard Cite

The human hand is involved in many computer vision tasks, such as hand posture estimation, hand movement identification, human activity analysis, and other similar tasks, in which hand detection is an important preprocessing step. It is still difficult to correctly recognize some hands in a cluttered environment because of the complex display variations of agile human hands and the fact that they have a wide range of motion. In this study, we provide a brief assessment of CNN-based object identification algorithms, specifically Densenet Yolo V2, Densenet Yolo V2 CSP, Densenet Yolo V2 CSP SPP, Resnet 50 Yolo V2, Resnet 50 CSP, Resnet 50 CSP SPP, Yolo V4 SPP, Yolo V4 CSP SPP, and Yolo V5. The advantages of CSP and SPP are thoroughly examined and described in detail in each algorithm. We show in our experiments that Yolo V4 CSP SPP provides the best level of precision available. The experimental results show that the CSP and SPP layers help improve the accuracy of CNN model testing performance. Our model leverages the advantages of CSP and SPP. Our proposed method Yolo V4 CSP SPP outperformed previous research results by an average of 8.88%, with an improvement from 87.6% to 96.48%.

show abstract

“…A component of this is making sure that the laws regarding face masks are followed. For instance, the combination of artificial intelligence models and surveillance technologies might be useful in this scenario [14,15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Medical Face Mask Recognition for COVID-19 Mitigation: Utilizing YOLO V5 Object Detection

Dewi¹,

Christanto²

2023

RIA

View full text Add to dashboard Cite

The ongoing COVID-19 pandemic has significantly affected global public health, necessitating protective measures such as wearing face masks to reduce the spread of the disease. Recent advances in deep learning-based object detection have shown promise in accurately recognizing objects within images and videos. In this study, the state-of-the-art You Only Look Once (YOLO) V5 object detection model was employed to classify individuals based on their mask-wearing status into three categories: none, poor, and adequate. YOLO V5 is known for its high efficiency and precision in object recognition tasks. Two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), were combined for simultaneous evaluation. The performance of the models was assessed based on crucial metrics such as Giga-Floating Point Operations (GFLOPS), workspace area, detection time, and mean average precision (mAP). Results indicated that the YOLO V5m model achieved the highest mAP (97.2%) for the "adequate" class, demonstrating its effectiveness in detecting proper mask usage for COVID-19 mitigation.

show abstract

Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks

Cited by 8 publications

References 25 publications

Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet

Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet

Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection

Automatic Medical Face Mask Recognition for COVID-19 Mitigation: Utilizing YOLO V5 Object Detection

Contact Info

Product

Resources

About