Object Identification and Localization of Visual Explanation for Weapon Detection
3
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85141807513
Journal Title
Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022
Start Page
144
End Page
147
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 144-147
Suggested Citation
Hnoohom N., Chotivatunyu P., Yuenyong S., Wongpatikaseree K., Mekruksavanich S., Jitpattanakul A. Object Identification and Localization of Visual Explanation for Weapon Detection. Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 144-147. 147. doi:10.1109/RI2C56397.2022.9910301 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84339
Title
Object Identification and Localization of Visual Explanation for Weapon Detection
Author's Affiliation
Other Contributor(s)
Abstract
Weapon detection is a difficult task that requires accurate identification of weapon objects in images. The object localization approach is mostly used because it combines a gradient with a convolutional layer to create a map of key locations on the image. This paper presents an Eigen-CAM method to localize and detect objects in an image for a Faster Region-based Convolutional Neural Network (Faster R-CNN) residual neural network (ResNet 50) model, giving a visual explanation. The Internet Movie Firearms Database (IMFDB) was used to train a deep learning model with the Faster R-CNN ResNet 50 model of the pre-trained PyTorch framework. Experimental results indicated that the Faster R-CNN ResNet 50 model achieved the highest mAP of 0.497 with 0.5 IoU. The Eigen-CAM method performed effectively for visual image representation.