Real-time license plate detection for non-helmeted motorcyclist using YOLO

Jamtsho, Yonten; Riyamongkol, Panomkhawn; Waranusast, Rattapoom

doi:10.1016/j.icte.2020.07.008

Cited by 60 publications

(14 citation statements)

References 5 publications

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“…Moreover, using bounding boxes may be more real-time capable. Thus, during the examination, a region of interest could be simultaneously analyzed with a changing transducer position [ 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

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Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

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Section: Discussionmentioning

confidence: 99%

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…Due to the quick advancement of deep learning, YOLO, SSD, RetinaNet, and Faster RCNN techniques were also adopted for training and testing on road signs/traffic light detection, road damage detection tasks [ 3 , 6 , 100 , 148 ]. Some new approaches, such as adversarial training, different backbone architecture, and attention mechanism, have been used to increase speed or improve detection processes in challenging and complex environments [ 66 , 72 , 88 , 100 , 109 , 135 ]. An accurate observation of the surrounding environment is required for an autonomous vehicle (AV) to help human beings to reach their destination safely.…”

Section: Applications Of Object Detectionmentioning

confidence: 99%

Tools, techniques, datasets and application areas for object detection in an image: a review

Kaur

Singh

2022

Multimed Tools Appl

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Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

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“…An example of detection using YOLO is license plate detection for non-helmeted motorcyclist [15] and there are more examples for detecting different objects using YOLO.…”

Section: Yolomentioning

confidence: 99%

Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network

Faraj

Boskany

2020

UHD J SCI TECH

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Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.

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Real-time license plate detection for non-helmeted motorcyclist using YOLO

Cited by 60 publications

References 5 publications

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Tools, techniques, datasets and application areas for object detection in an image: a review

Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network

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