Romadlon, Muhammad Rizqi (2025) Yolov8 implementation on british sign language system with edge detection extraction / Muhammad Rizqi Romadlon</p>. Diploma thesis, Universitas Negeri Malang.
Full text not available from this repository.Abstract
p This study presents the development and implementation of a deep learning-based system for recognizing static hand gestures in British Sign Language (BSL). The system utilizes the YOLOv8 model in conjunction with edge detection extraction techniques. The objective of this study is to enhance the accuracy of recognition and facilitate communication for individuals with hearing impairments. The dataset was obtained from Kaggle and comprises images of various BSL hand signs captured against a uniform green background under consistent lighting conditions. The preprocessing steps entailed resizing the images to 640x640 pixels implementing pixel normalization filtering out low-quality images and employing data augmentation techniques such as horizontal flipping rotation shear and brightness adjustments to enhance robustness. Edge detection was implemented to accentuate the contours of the hand thereby facilitating more precise gesture identification. Manual annotation was performed to generate both bounding boxes and segmentation masks allowing for the training of two model variants The first is YOLOv8 (non-segmentation) and the second is YOLOv8-seg (segmentation). Both models underwent training over a period of 100 epochs employing the Adam optimizer and binary cross-entropy loss. The training-to-testing data splits utilized were 50 50 60 40 70 30 and 80 20. The evaluation metrics employed included mAP 50 precision recall and F1-score. The YOLOv8-seg model with an 80 20 split demonstrated the optimal performance exhibiting a precision of 0.974 a recall of 0.968 and mAP 50 of 0.979. These metrics signify the model s capacity for robust detection and localization. Despite requiring greater computational resources the segmentation model offers enhanced contour recognition rendering it well-suited for high-precision applications. However the generalizability of the model is constrained due to the employment of static gestures and controlled backgrounds. In the future researchers should consider incorporating dynamic gestures varied backgrounds and uncontrolled lighting to enhance real-world performance. /p
| Item Type: | Thesis (Diploma) |
|---|---|
| Divisions: | Fakultas Teknik (FT) > Departemen Teknik Elektro (TE) > S1 Teknik Informatika |
| Depositing User: | library UM |
| Date Deposited: | 11 Sep 2025 04:29 |
| Last Modified: | 09 Sep 2025 03:00 |
| URI: | http://repository.um.ac.id/id/eprint/426300 |
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