scholarly journals Deteksi Kendaraan secara Real Time menggunakan Metode YOLO Berbasis Android

2020 ◽  
Vol 9 (1) ◽  
pp. 8-14
Author(s):  
Junita Sri Wisna Hutauruk ◽  
Tekad Matulatan ◽  
Nurul Hayaty
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Training Process ◽  
Congestion Problems ◽  
Bounding Boxes

The Activities on the highway that involve vehicles often have congestion problems due to the tightening of the quantity of vehicles on the highway. In addition, there are also problems of order and violations, the use of improper routes, such as vehicles entering the lane that are not intended for the vehicle. Therefore, researchers designed a vehicle detection application in real time based on Android using the YOLO method (You Only Look Once). The analysis carried out using 200 datasets, 4 classes, 10 batches, and 200 epochs. The training process was carried out up to 4000 steps, and the storage of checkpoints to the form of file protob was done at steps 800, 1000, 1200, 1400, 1600, 1800, 2000, 3000, and 4000. Bounding boxes successfully detected and classified objects correctly. This test is done using a Xiaomi Redmi 4X smartphone with a video resolution measuring 768x432 pixels.

Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images

2019 ◽  
Author(s):  
Nils Gahlert ◽  
Jun-Jun Wan ◽  
Michael Weber ◽  
J. Marius Zollner ◽  
Uwe Franke ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Rgb Images ◽  
Bounding Boxes

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

2021 ◽  
pp. 1-13
Author(s):  
Andres Bell ◽  
Tomas Mantecon ◽  
Cesar Diaz ◽  
Carlos R. del-Blanco ◽  
Fernando Jaureguizar ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Time Performance

A Fast Evolutionary Algorithm for Real-Time Vehicle Detection

2013 ◽  
Vol 62 (6) ◽  
pp. 2453-2468 ◽  
Author(s):  
Vinh Dinh Nguyen ◽  
Thuy Tuong Nguyen ◽  
Dung Duc Nguyen ◽  
Sang Jun Lee ◽  
Jae Wook Jeon
Keyword(s):  
Evolutionary Algorithm ◽  
Real Time ◽  
Vehicle Detection

scholarly journals Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Heavy Vehicles ◽  
Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

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