Bangla Sign Language Detection System Using YOLOv5 Computer Vision Project

The Bangla Sign Language Detection System is an innovative computer vision project aimed at developing a robust and efficient system to detect Bangla sign language gestures, specifically focusing on digits and alphabets. This project utilizes the power of YOLOv5, a state-of-the-art object detection algorithm, to achieve high accuracy and real-time performance.

Objective:

The primary objective of this project is to create a computer vision model that can accurately recognize and interpret Bangla sign language gestures for digits and alphabets. The system aims to facilitate communication for individuals with hearing and speech impairments, enabling them to interact effectively with others using sign language.

Methodology:

The project employs the YOLOv5 architecture, which is renowned for its speed, accuracy, and ease of implementation. YOLO (You Only Look Once) is a single-shot object detection model that simultaneously predicts bounding boxes and class probabilities for each object in an image. The model's architecture allows it to achieve real-time performance on various hardware platforms.

Performance Metrics:

The Bangla Sign Language Detection System using YOLOv5 has achieved remarkable performance in terms of accuracy and precision, with the following metrics:

96.4% mAP (Mean Average Precision): This metric measures the average precision across all classes and detection confidence thresholds, reflecting the overall accuracy of the model's predictions.

94.8% Precision: Precision measures the proportion of true positive detections out of all positive detections, indicating the model's ability to avoid false positives.

93.4% Recall: Recall, also known as sensitivity, measures the proportion of true positive detections out of all ground truth positive instances, indicating the model's ability to avoid false negatives.

Dataset:

To train the YOLOv5 model, a comprehensive and diverse dataset of Bangla sign language gestures for digits and alphabets was collected and annotated. The dataset includes various hand orientations, lighting conditions, and backgrounds, ensuring the model's robustness in real-world scenarios.

Implementation:

The project's implementation involves the following key steps:

Data Preprocessing: Cleaning, resizing, and augmenting the dataset to ensure diversity and mitigate overfitting.

Model Training: Utilizing YOLOv5 and training the model on the annotated dataset, tuning hyperparameters for optimal performance.

Model Evaluation: Validating the trained model using a test set to measure performance metrics such as mAP, precision, and recall.

Inference: Deploying the trained model in real-time applications to detect Bangla sign language gestures.

Expected Impact:

The Bangla Sign Language Detection System using YOLOv5 has the potential to make a significant impact on the lives of individuals with hearing and speech impairments in Bangladesh. By accurately recognizing and interpreting sign language gestures, the system will facilitate smoother communication between the hearing-impaired community and the general population, fostering inclusivity and understanding.

Future Scope:

The project opens doors for further research and development in the domain of sign language detection. Future improvements may involve expanding the system to recognize more complex sign language expressions, incorporating finger-spelling, or integrating natural language processing for real-time translation of sign language into text or speech.

The Bangla Sign Language Detection System using YOLOv5 stands as a testament to the potential of computer vision technology to bridge communication gaps and empower individuals with diverse abilities. With its impressive accuracy and precision, the system paves the way for more accessible and inclusive societies.