Detection of Motorcycles on Highways Using Faster R-CNN Based on VGG16

Moch Dian Lazuardi Yudha, Wawan Setiawan, Yaya Wihardi

Abstract


This research aims to develop an object detection system with input in the form of image data in free size. The development of an object detection system model was carried out by applying Machine Learning to overcome object detection in an image using the Faster R-CNN method based on the VGG16 algorithm. The system developed produces a bounding box for an object in the image. System development was carried out in the Python programming language by utilizing several libraries such as Keras. Experiments were carried out by measuring the loss value of the training data entered into the system. The experimental results show that the resulting information is proven to be able to detect objects in a given image. This system can produce information based on image data that has been trained with this system. This study used two experiments which obtained a loss value of 0.0601 in the first study and 0.1211 in the second study.

Keywords


Computer vision; Convolutional neural networks; Faster RCNN; Machine learning; Regional proposal network; ROI pooling; VGG16

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References


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DOI: https://doi.org/10.17509/jcs.v4i1.71179

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