Object Detection System – AI-powered assistive object recognition solution
An AI-based object detection system designed to assist visually impaired users by identifying real-world objects in real time.
An AI-based object detection system designed to assist visually impaired users by identifying real-world objects in real time.
The Blind Object Detection system identifies more than 80 everyday objects. These include pens, people, mugs and books. As well as mobiles and chairs. It works on the YOLO model to detect objects within a 2-5-meter range. The system supports speech-to-text input. This allows users to specify the object they want to detect by enhancing usability through computer vision technology.
Detection System
AI, ML
Computer Vision System
Training a machine learning model for blind object detection required handling large datasets. At the same time, it had to ensure accuracy and speed. Over 1000 images per object were used for training by making script execution time intensive. Avoiding conflicts between similar objects during detection was critical. Achieving real-time object localization along with identification added further complexity.
Develop an AI-driven object detection system. It should be capable of identifying predefined objects accurately to assist visually impaired users in real-world environments.
Enable fast and precise recognition of objects along with their position using computer vision and deep learning models.
Design the system to function as an assistive solution that improves self-orientation and independence for visually challenged users.
Ensure the solution can be embedded into wearable or mobile applications for wider accessibility and practical usage.
Yudiz created a Blind Object Detection System with YOLO. The algorithm uses deep learning and CNNs for object recognition. The model evaluates bounding boxes and class probabilities to identify objects from full images with high accuracy.
The solution was built using Python and OpenCV. This enabled fast processing and real-time detection. YOLO uses a single neural network for complete detection. This design keeps the system fast. It ensures accuracy within the specified distance range.
The system supports wearable integration. It also enables assistive applications. This makes it suitable for real‑world use cases. Especially where visually impaired users need immediate feedback and object awareness.
Collected and trained the model using thousands of labeled images per object to ensure reliable detection accuracy.
Integrated YOLO with OpenCV and Python to enable real-time object recognition using deep learning techniques.
Tested detection accuracy, object conflicts and distance parameters to make sure that real-time performance is stable.
Prepared the system for integration into assistive and wearable applications for practical deployment.
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