Manuscript received October 18, 2022; revised February 3, 2023; accepted March 13, 2023.
Abstract—This study focuses on identifying and detecting several types of vehicles, with each vehicle’s position depicted by drone technology or an Unmanned Aerial Vehicle (UAV) camera. The vehicle’s position is captured from a height of 350 to 400 meters above the ground. This study aims to identify the class of vehicles that travel on the highway. The experiment employs several convolutional neural network models, including YOLOv4, YOLOv3, YOLOv7, DenseNet201-YOLOv3, and CSResNext50-Panet-SPP, to identify this type of vehicle. Meanwhile, the Darknet algorithm aids the training process by making it easier to identify the type of vehicle depicted in MP4 movies. Several other Convolution Neural Network (CNN) model experiments were conducted in this study, but due to hardware limitations, only these 5 CNN models could produce an optimal accuracy of up to 70%. Following several experiments, the CSResNext50-Panet-SPP model produced the highest accuracy while detecting 100% of video data using UAV technology, including the volume of vehicles detected while crossing the road. Other CNN models produced high accuracy values, such as DenseNet201- YOLOv3 and YOLOv4 models, which can detect up to 98% to 99% of the time. This research can improve its capabilities by detecting other classes that are affordable by UAV technology but require hardware and peripheral technology to support the training process.
Keywords—unmanned aerial vehicle, vehicle, Convolution Neural Network (CNN), CSResNext50-Panet-SPP, densenet201-YOLOv3, YOLOv3, YOLOv4, YOLOv7
Cite: Abdul Haris Rangkuti, Varyl Hasbi Athala, and Farrel Haridhi Indallah, "Development of Vehicle Detection and Counting Systems with UAV Cameras: Deep Learning and Darknet Algorithms," Journal of Image and Graphics, Vol. 11, No. 3, pp. 248-262, September 2023.
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