Real Time Object Detection using Deep Learning and SSD Algorithm

Real Time Object Detection using Deep Learning and SSD Algorithm

Prof. Yogesh N, Surya Kant, Talluri Gayathri, U P Akshitha, Vinay Kumar Singh

STUDENTS OF DEPARTMENT INFORMATION SCIENCE AND ENGINEERING

ACHARYA INSTITUTE OF TECHNOLOGY, SOLADEVANAHALLI. 560107

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Abstract – Real time object detection is a complex area of computer vision, vast and vibrant. If there is a single object to be detected in an image, it is known as Image Localization and if there are multiple objects in an image, then it is Object Detection. Real time object identification to find and recognize real objects such as cars, bicycles, TV, flowers and people from pictures or videos. Object identification technique allows you to find out image or video details, as it allows detection, localization and multiple detection Objects in pictures or videos. Identifying objects in a video or image stream can be done through processes such as pre-processing. segmentation, foreground and background extraction, feature extraction. We will try to add new features related to the real world in future versions. In today's scenario, the Single Shot Multi-Box Detector (SSD) algorithm is considered the fastest approach for object detection using a single layer of a convolutional network. In our paper, we have focused on enhancing the classification accuracy of object detection while maintaining the algorithm's speed. Experimental results have confirmed that our proposed improved SSD algorithm achieves high accuracy.