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Adiratna Ciptaningrum
Politeknik Negeri Madiun
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Journal : Journal Geuthee of Engineering and Energy

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Design of smart human following on rail inspection using human pose estimation marker-less motion capture based on blazepose Ciptaningrum, Adiratna
Journal Geuthee of Engineering and Energy Vol 2, No 2 (2023): Journal Geuthee of Engineering and Energy
Publisher : Geuthèë Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52626/joge.v2i2.26

Abstract

Advances in artificial intelligence (AI) technology today have a significant impact in various aspects of human life. One example is the evolution of robotics that has achieved the ability to follow human movements. To achieve this, AI technology utilizes image recognition through Computer Vision and the Human Pose Estimation method with the help of the BlazePose library, which is able to recognize 33 keypoints in human body poses. Research in this area aims to develop an automatic control system that can be used on inspection carts, enabling them to follow human body movements while walking. The results showed a detection accuracy rate of 84.82% with an optimal detection distance between 4 to 8 meters from the camera, with an average detection accuracy of 89.862%. On the motor control aspect, the system is set to turn off the motor when the distance between the device and the object is in the range of 1-2 meters, and turn it on at a distance of 3-12 meters. However, it is important to note that the accuracy achieved is greatly affected by the color segmentation capabilities of the software, the lighting conditions in the environment, as well as the resolution of the camera used.
Data-Driven Anomaly Control Detection for Railroad Lines Using Sobel Filter and VGG-16 Model, Res-Net50, InceptionV3 Ciptaningrum, Adiratna; Apriyanto, R. Akbar Nur; Prakoso, Dimas Nur; Yudha, R. Gaguk Pratama; Echsony, Mohammad Erik
Journal Geuthee of Engineering and Energy Vol 2, No 1 (2023): Journal Geuthee of Engineering and Energy
Publisher : Geuthèë Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52626/joge.v2i1.17

Abstract

Rail inspection is essential to ensure the safety and performance of the rail system. In rail inspection, object detection is an important task to locate and identify damage or obstructions on the rail. This research discusses the use of Sobel features on three convolutional neural network (CNN) architectures, namely VGG-16, ResNet50, and InceptionV3 for object detection in rail inspection. The purpose of this research is to improve the accuracy of object detection in rail inspection by utilizing the edge information obtained from the Sobel filter. This research involves several stages, namely collecting rail image data, image processing with the Sobel filter, feature extraction using three CNN architectures, namely VGG-16, ResNet50, and InceptionV3, and evaluating object detection performance using accuracy metrics. The results show that the use of Sobel features in the three CNN architectures can improve the accuracy of object detection in rail inspection. The evaluation results show that the ResNet50 model provides the best performance with detection accuracy reaching 96%, followed by the InceptionV3 model with 90% accuracy, and the VGG-16 model with 90% accuracy. Based on the results of this study, it can be concluded that the use of Sobel features in CNN architecture can improve object detection accuracy in rail inspection. In addition, the ResNet50 model has the best performance compared to the VGG-16 and InceptionV3 models in object detection in rail inspection. This can be a reference in the development of future rail inspection object detection systems.
Co-Authors Alfiyani, Risma Apriyanto, R. Akbar Nur Arum, Widya Sekar Bi Asngali Dimas Nur Prakoso Edo Zulmi Faikhsan Faikhsan, Edo Zulmi Fita Sari Ida Ayu Putu Sri Widnyani Imam Junaedi Jamil, Zhulfi Fadhillah Mudmainah, Putri Hana Widyaning Nadia, Hana Jasmine Pangestu, Djadug Suryo Putri, Larissa Kartika R. Akbar Nur Apriyanto R. Gaguk Pratama R. Oktav Yama Hendra R. Oktav Yama Hendra Rahayu Mekar Bisono Rahayu Mekar Bisono, Rahayu Mekar Saskya, Lorensa Firda Sembiring, Rinawati Syah Putra, Muhammad Dio Wahyu Pribadi Wahyu Pribadi Wicaksono, Darma Arif Wicaksono, Ramadhana Eka Wida Yuliar Rezika, Wida Yuliar Yudha, R. Gaguk Pratama Yudhiatama, Galang Dafa