A IoT-enabled Obstacle Detection and Recognition Technique for Blind Persons
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Object detection is a critical task in computer vision, essential for real-time applications ranging from autonomous vehicles to surveillance systems. This proposed work presents a comparative evaluation of Single Shot Multibox Detector (SSD algorithms). YOLOv3, MobileNetv3, RetinaNet and Faster R-CNN, in the context of real-time obstacle detection from camera images. The study evaluates these algorithms on performance metrics Precision, Recall and F1 score across various Intersection Over Union (IoU) thresholds. Also, the computational efficiency in terms of the time taken per frame is assessed to determine the effectiveness of each algorithm. The workflow includes image processing, augmentation, and application of SSD models to detect objects like vehicles, pedestrians and traffic signals. Results indicate that YOLOv3 achieves the highest precision of 96% demonstrating robust performance in real-time scenarios, while MobileNetv3 follows closely with 92%, RetinaNet and Faster RCNN achieves accuracy 90% and 90% respectively. These findings contribute to understanding the trade-offs between accuracy and computational efficiency in selected suitable SSD models for practical deployment.