Optimization of Vehicle Object Detection Based on UAV Dataset: CNN Model and Darknet Algorithm

Abdul Rangkuti - Bina Nusantara University, Jakarta, Indonesia
Varyl Athala - Bina Nusantara University, Jakarta, Indonesia

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DOI: http://dx.doi.org/10.30630/joiv.7.1.1159


This study was conducted to identify several types of vehicles taken using drone technology or Unmanned Aerial Vehicles (UAV). The introduction of vehicles from above an altitude of more than 300-400 meters that pass the highway above ground level becomes a problem that needs optimum investigation so that there are no errors in determining the type of vehicle. This study was conducted at mining sites to identify the class of vehicles that pass through the highway and how many types of vehicles pass through the road for vehicle recognition using a deep learning algorithm using several CNN models such as Yolo V4, Yolo V3, Densenet 201, CsResNext –Panet 50 and supported by the Darknet algorithm to support the training process. In this study, several experiments were carried out with other CNN models, but with peripherals and hardware devices, only 4 CNN models resulted in optimal accuracy. Based on the experimental results, the CSResNext-Panet 50 model has the highest accuracy and can detect 100% of the captured UAV video data, including the number of detected vehicle volumes, then Densenet and Yolo V4, which can detect up to 98% - 99%. This research needs to continue to be developed by knowing all classes affordable by UAV technology but must be supported by hardware and peripheral technology to support the training process.


UAV; Vehicle; CNN Model; CsresNext-Panet50; Densenet201; Yolo V3; Yolo V4

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