Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips

Dewi, Christine; Chen, Rung-Ching; Zhuang, Yong-Cun; Jiang, Xiaoyi; Yu, Hui

doi:10.3390/bdcc7010054

Cited by 12 publications

(2 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these tasks can be challenging due to variations in lighting conditions, weather, and road surface conditions. Thus, ongoing research is being conducted to improve the accuracy and reliability of road marking detection and recognition algorithms [4].…”

Section: Introductionmentioning

confidence: 99%

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

Chen,

Dewi,

Zhuang

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

In recent years, artificial intelligence has led to rapid development and application across various industries, which has prompted this. One of the significant developments is the improvement of transportation methods. Accidents involving vehicles frequently result in a high number of fatalities as well as economic damage. Road detection is one of the applications that can be used by self-driving cars. Traffic accidents happen, but artificial intelligence is used in many nations to construct smart cities and apps for selfdriving cars. Since public road sign datasets have been used in significant research for road sign identification and analysis, these datasets are particularly significant for training autonomous vehicles. This study records the roads of various cities in Taiwan through road driving. It manually collects traffic signs in Taiwan to create a data set of road signs in Taiwan in daytime environments as well as nighttime environments. Since there is currently no data set of road signs in Taiwan, this study is necessary to create a data set of road signs in Taiwan. The YOLO model is utilized in this work to design road signs in Taiwan for mark detection. The techniques of Contrast Stretching (CS), Histogram Equalization (HE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) are evaluated in a nighttime setting and compared to the original image captured at night. The experimental results show that the best model during the day is YOLO V4 (no flip), the test set mAP is 86.77%, the Precision is 82%, the Recall is 87%, the F1-score is 84%, and the IoU is 63.92%. At night, the CLAHE image method works best in the YOLOv5x model, with a mAP of 86.40%. And YOLOv5 can be used in mobile devices or embedded devices, so this study recommends using CLAHE's YOLOv5x as the best model at night and used to improve the effect of road sign detection at night.

show abstract

Section: Introductionmentioning

confidence: 99%

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

Chen,

Dewi,

Zhuang

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…This data can be analysed to identify patterns and make informed decisions regarding public health interventions. By understanding where and when mask usage is low, authorities can implement targeted educational campaigns, allocate resources, and adjust their strategies accordingly [12].…”

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

Advancing Colon Cancer Detection: A YOLOv5-Based Approach with Emphasis on Precision, Interpretability, and Real-World Deployment Considerations

Jaware,

Patil,

Badgujar

2024

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips

Cited by 12 publications

References 56 publications

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

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

Advancing Colon Cancer Detection: A YOLOv5-Based Approach with Emphasis on Precision, Interpretability, and Real-World Deployment Considerations

Contact Info

Product

Resources

About