Performance Comparison of MobileNet SSD and YOLO v4 in Object Detection

Object recognition is a challenging computer vision application that finds wide use in various fields such as autonomous cars, robotics, security tracking and guiding visually impaired individuals. People with visual impairments face limitations in their mobility, making it crucial to rely on technology to assist them. By training our technologies to recognize objects, can provide guidance to blind individuals when needed. MobileNet SSD is an object detection model that determines the bounding box and category of an object in an input image. Another popular algorithm in object detection is YOLO v4 (You Only Live Once), which has seen significant advancements. The primary objective of this paper is to compare the MobileNet algorithm, which serves as the backbone for Single Shot Detector (SSD), with the YOLO v4 Algorithm. YOLO v4 utilizes an intricate Convolutional Neural Network architecture called Darknet. The goal is to determine the best model that can convert images to text and then text to speech for visually impaired individuals, allowing them to live independently. The chosen model will also provide audio responses by converting annotated text into audio and provide the location of objects in the camera's view. The results of image recognition will be communicated to the user through system audio feedback. Its experimented on real time videos and noticed that accuracy of object detection in YOLO v4 is more compared to MobileNet SSD.