A face detection method via ensemble of four versions of YOLOs

Khalili, Sanaz; Shakiba, Ali

doi:10.1109/mvip53647.2022.9738779

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Value" refers to both the license of specific information and any record of an asset license (see Fig. 4) [18].…”

Section: Distribution Of Marks Using Blockchainmentioning

confidence: 99%

An Innovative and Secure Platform for Leveraging the Blockchain Approach for Online Exams

Manawar

2023

att

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Numerous educational establishments now use the online platform to conduct tests, particularly for students at the secondary and tertiary levels. Candidates are given a username and password by the most popular online test program, and they then log in to the designated page to answer the questions. However, there are numerous flaws in this system, and cheating on the test can be done by misusing the password. This demonstrates the significance of implementing a secure system to avoid this issue. A blockchain framework that safeguards the online examination system is presented in this paper. An educational data-connecting data management system has been secured using the proposed framework. Without the need for a copy from the central servers, institutions can simply compile their data history. Data security is improved and any potential for cheating between users or third-party institutions that access applications and services is eliminated by the proposed blockchain framework. This study provides a secure framework for conducting and evaluating subject tests to guarantee consistency between students and servers and secure questionnaire delivery from servers.

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“…Value" refers to both the license of specific information and any record of an asset license (see Fig. 4) [18].…”

Section: Distribution Of Marks Using Blockchainmentioning

confidence: 99%

An Innovative and Secure Platform for Leveraging the Blockchain Approach for Online Exams

Manawar

2023

att

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“…Pada YOLOv1, dengan 24 lapisan konvolusi (convolutional layer) yang diikuti oleh 2 lapisan yang terhubung penuh (fully connected layer), sistem sudah dapat mendeteksi objek namun beberapa objek kecil tidak dapat terdeteksi oleh sistem [14][15] [16]. Lalu untuk meningkatkan kemampuan deteksinya, maka dibuatlah YOLOv2 yang merupakan hasil peningkatan dari YOLOv1 dengan meningkatkan batch normalization, menaikkan resolusi klasifikasi, serta menggunakan 19 convolution layer dengan 5 max pooling layer [17][18] [19].…”

Section: Pendahuluanunclassified

Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B

Sanubari

Puriyanto²

2022

Buletin Ilmiah Sarjana Teknik Elektro

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This research is a form of development of object detection capabilities on wheeled soccer robots using an omnidirectional camera with the You Only Look Once (YOLO) method where the results show that the robot can detect more than one object, namely the ball and the goal on the green field. This study uses the KRSBI-Wheeled UAD robot using an omnidirectional camera as a tool to carry out the detection process and then uses OpenCV 4.0, Deep Learning, and a laptop as a place to create a detection model, as well as balls and goals as objects to be detected. The results obtained from this study are that the two types of YOLO models tested, namely YOLOv3 and YOLOv3-Tiny can detect ball and goal objects in two different types of frame sizes, namely 320x320 and 416x416 which can be seen from the performance of the YOLOv3 model which has an mAP value of 76%. on the 320x320 frame and an mAP value of 87.5% in the 416x416 frame then the YOLOv3-Tiny model has an mAP value of 68.1% in the 320x320 frame and an mAP value of 75.5% in the 416x416 frame where the YOLOv3 model can detect both object class is much more stable compared to YOLOv3-Tiny. Penelitian ini merupakan bentuk pengembangan dari kemampuan deteksi objek pada robot sepak bola beroda dengan menggunakan kamera omnidirectional dengan metode You Only Look Once (YOLO) dimana hasil penelitian menunjukkan bahwa robot dapat mendeteksi lebih dari satu objek yaitu bola dan gawang di atas lapangan hijau. Penelitian ini menggunakan robot KRSBI-Beroda UAD dengan memakai kamera omnidirectional sebagai alat untuk melakukan proses pendeteksian lalu menggunakan OpenCV 4.0, Deep Learning, dan laptop sebagai tempat membuat model pendeteksian, serta bola dan gawang sebagai objek yang akan dideteksi. Hasil yang didapatkan dari penelitian ini yaitu kedua jenis model YOLO yang diuji yaitu YOLOv3 dan YOLOv3-Tiny dapat mendeteksi objek bola dan gawang pada dua jenis ukuran frame yang berbeda yaitu 320x320 dan 416x416 yang dapat dilihat dari performa pada model YOLOv3 memiliki nilai mAP sebesar 76% pada frame 320x320 dan serta nilai mAP sebesar 87,5% pada frame 416x416 lalu pada model YOLOv3-Tiny memiliki nilai mAP sebesar 68,1% pada frame 320x320 serta nilai mAP sebesar 75,5% pada frame 416x416 yang dimana model YOLOv3 dapat mendeteksi kedua kelas objek jauh lebih stabil dibandingkan dengan model YOLOv3-Tiny.

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“…This idea omits the complex candidate box selection and calculation steps in the two-stage algorithm. Therefore, the running speed of the algorithm is far faster than that of the two-stage detection algorithm [ 27 , 28 , 29 ]. The representative algorithms are the YOLO algorithm and the SSD algorithm.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7-RAR for Urban Vehicle Detection

Zhang

Sun

Wang

et al. 2023

Sensors

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Aiming at the problems of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban roads, weak perception of small targets in perspective, and insufficient feature extraction, the YOLOv7-RAR recognition algorithm is proposed. The algorithm is improved from the following three directions based on YOLOv7. Firstly, in view of the insufficient nonlinear feature fusion of the original backbone network, the Res3Unit structure is used to reconstruct the backbone network of YOLOv7 to improve the ability of the network model architecture to obtain more nonlinear features. Secondly, in view of the problem that there are many interference backgrounds in urban roads and that the original network is weak in positioning targets such as vehicles, a plug-and-play hybrid attention mechanism module, ACmix, is added after the SPPCSPC layer of the backbone network to enhance the network’s attention to vehicles and reduce the interference of other targets. Finally, aiming at the problem that the receptive field of the original network Narrows, with the deepening of the network model, leads to a high miss rate of small targets, the Gaussian receptive field scheme used in the RFLA (Gaussian-receptive-field-based label assignment) module is used at the connection between the feature fusion area and the detection head to improve the receptive field of the network model for small objects in the image. Combining the three improvement measures, the first letter of the name of each improvement measure is selected, and the improved algorithm is named the YOLOv7-RAR algorithm. Experiments show that on urban roads with crowded vehicles and different weather patterns, the average detection accuracy of the YOLOv7-RAR algorithm reaches 95.1%, which is 2.4% higher than that of the original algorithm; the AP50:90 performance is 12.6% higher than that of the original algorithm. The running speed of the YOLOv7-RAR algorithm reaches 96 FPS, which meets the real-time requirements of vehicle detection; hence, the algorithm can be better applied to vehicle detection.

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A face detection method via ensemble of four versions of YOLOs

Cited by 6 publications

References 10 publications

An Innovative and Secure Platform for Leveraging the Blockchain Approach for Online Exams

An Innovative and Secure Platform for Leveraging the Blockchain Approach for Online Exams

Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B

YOLOv7-RAR for Urban Vehicle Detection

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