YOLO-Based Valve Type Recognition and Localization

Mahalleh, Vahid Behtaji Siahkal; AL-Qutami, Tareq Aziz; Mahmood, Iskandar Al-Thani

doi:10.1109/iea.2019.8715012

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…In addition, the model was able to establish the type of valve labels based on a single frame. 16 Adarsh Dataset of Banknotes is made in different specifications as rotation, cluttered background, illumination level, occlusion, scaling, and so forth. 17…”

Section: Related Workmentioning

confidence: 99%

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

Francies

Ata

Mohamed

2021

Concurrency and Computation

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A multiclass 3D object recognition has perceived a numerous evolution with respect to both accuracy and speed. This study introduces the implementation of modern YOLO algorithms (YOLOv3, YOLOv4, and YOLOv5) for multiclass 3D object detection and recognition. All YOLO algorithms have been tested according to a very large scaled dataset (Pascal VOC dataset). Performance evaluation has targeted the calculation of the following metrics; mAP (mean average precision), recall, F1-score, IOU (intersection over union), and the running time. Experimental results demonstrate that the YOLOv3 has targeted mAP of 77%, IOU of 0.41 and the total running time was almost 8 h. Moreover, in YOLOv4, it has targeted mAP of 55%, IOU of 0.035 and the total running time nearly 7 h. In addition, YOLOv5 has established the mAP of 48%, IOU of 0.045, and the total running time was about 3 h. Finally, a modified version of YOLOv5 has been proposed in the state-of-the-art of optimizing its hyperparameters and layering system. Accordingly, the mAP scored about 55% with 3 h running time. The final conclusions of this study have demonstrated that YOLOv3 has scored the highest recognition accuracy, however, the proposed modified YOLOv5 has scored the least processing time.

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Section: Related Workmentioning

confidence: 99%

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

Francies

Ata

Mohamed

2021

Concurrency and Computation

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Handwritten Logic Circuits Analysis Using the YOLO Network and a New Boundary Tracking Algorithm

et al. 2022

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Handwriting analysis has been addressed by researchers for decades, and many advances were achieved in understanding handwritten texts so far. However, some applications have been rarely discussed. One of these applications that has received less attention is the understanding and analyzing of handwritten circuits. Today, with the widespread use of intelligent tools in engineering and educational processes, the need for new and accurate solutions for processing such handwritings is felt more than ever. This paper presents a new method to analyze handwritten logic circuits. In this method, circuit components are first identified using a deep neural network based on YOLO. Then, the connection among these components is recognized using a new simple boundary tracking method. Then, the binary function related to the handwritten circuit is obtained. Finally, the truth table of the logic circuit is generated. We have also created a set of various handwritten logic circuits called JSU-HWLC. The results of the experiments show the proper performance of the proposed method on the collected dataset. The experiments demonstrated that the YOLO algorithm achieved better results than other deep learning methods such as faster R-CNN, Detectron2, and RetinaNet. For this reason, YOLO has been used to identify logic gates in the proposed system.

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Design, Implementation and Evaluation of Ultrasonic Measurement System using ROS and MQTT

Mahalleh

Chand

Rahman

2020

2020 IEEE 8th Conference on Systems, Process and Control (ICSPC)

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YOLO-Based Valve Type Recognition and Localization

Cited by 5 publications

References 7 publications

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

Handwritten Logic Circuits Analysis Using the YOLO Network and a New Boundary Tracking Algorithm

Design, Implementation and Evaluation of Ultrasonic Measurement System using ROS and MQTT

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