Smartphone-based real-time object recognition architecture for portable and constrained systems

Martinez‐Alpiste, Ignacio; Golkarnarenji, Gelayol; Wang, Qi; Calero, José M. Alcaraz

doi:10.1007/s11554-021-01164-1

Cited by 30 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…All the information about optimization comparison can be seen in Table VI Figure 12 illustrates that the model inference time is needed to determine whether the reduced network size also reduces the inference time. Inference time calculates the time between the captured frame and the process until it results in data in terms of object detection [30]. The bigger the inference time, the slower the detection becomes.…”

Section: A Resultsmentioning

confidence: 99%

Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology

Rangkuti

Athalaa

Indallah

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

This study describes the optimization of hand gesture recognition on Raspberry Pi 4 technology has advanced over the past years, some computers are now able to compute much more complex problems like real-time object detection. But for small devices, optimization is required to run in real-time with acceptable performance in terms of latency and low-cost effect on accuracy. Low latency is a requirement for most technology, especially when integrating real-time object detection as input into Self-Service Technology on Raspberry Pi for the store. This research was conducted on 288 pictures with six types of chosen hand gestures for command inputs that have been configured in the Self-Service Technology as a training dataset. In the experiment carried out with 5 CNN object detection models were used, namely YOLOv3-Tiny-PRN, YOLOv4-Tiny, MobileNetV2-Yolov3-NANO, YOLO-Fastest-1.1, and YOLO-Fastest-1.1-XL. Based on the experiment after optimization, the FPS and inference time metrics have improved performance. The performance improves due to a gained average value of FPS by 3 FPS and a reduced average value of inference time by 119,260 ms. But such an improvement also comes with a reduction in overall accuracy. The rest of the parameters have a reduced score on Precision, Recall, F1-Score, and some for IoU. Only YOLO-Fastest-1.1-XL have an improved value of IoU by about 0.58%. Some improvements in the CNN and dataset might improve the performance even more without sacrificing too much on the accuracy, but it's most likely suitable for another research as a continuation of this topic.

show abstract

Section: A Resultsmentioning

confidence: 99%

Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology

Rangkuti

Athalaa

Indallah

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…We have compared the two approaches: a localised solution based on the smartphone [13], [14] and the NetApp solution. Table II presents the comparison results in terms of model size, loading time, and inference time of the employed scenarios.…”

Section: B Quantitative Resultsmentioning

confidence: 99%

NetApps Approach for Accelerating Vertical Adoption of 5G Networks: A UAV Case

Martinez‐Alpiste

Golkarnarenji

Klonidis

et al. 2022

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

View full text Add to dashboard Cite

Whilst the Fifth Generation (5G) mobile networks are being deployed worldwide, the adoption of this next-generation networking paradigm by the vertical businesses such as the 4th industrial revolution (Industry 4.0) sectors is lagging. One of the empirical barriers for the verticals to embrace 5G rapidly is the lack of easy-toaccess platforms that facilitate cost-efficient deployment of their Network Applications (NetApps) to create endto-end services. This paper presents a novel and realistic NetApp platform to remove this barrier, thereby speeding up the smooth vertical businesses' transition to 5G and beyond networks. Particularly, the paper provides a vision and insights on an innovative Unmanned Aerial Vehicle (UAV) based Industry 4.0 NetApp to cast lights on how 5G NetApps can help shape the resultant new business models and open new business opportunities. In this case, the NetApp is able to improve the results by detecting intruders in real time and reducing the delay of detection by 226 ms (38%).

show abstract

“…The system draws a box around the object and displays the detection accuracy. Similarly, the authors of Bian et al (2021) and Martinez-Alpiste et al (2022) presented a variety of approaches for CV, including OpenCV and SSD-MobileNet, object recognition, and so on.…”

Section: Related Workmentioning

confidence: 99%

Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Wahab

Ullah²,

Shah³

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

Computer vision (CV) and human–computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object’s class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system’s accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

show abstract

Smartphone-based real-time object recognition architecture for portable and constrained systems

Cited by 30 publications

References 24 publications

Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology

Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology

NetApps Approach for Accelerating Vertical Adoption of 5G Networks: A UAV Case

Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Contact Info

Product

Resources

About