Real-Time Object Detection Using Pre-Trained Deep Learning Models MobileNet-SSD

Mobile networks and binary neural networks are the most commonly used techniques for modern deep learning models to perform a variety of tasks on embedded systems. In this paper, we develop a technique to identify an object considering the deep learning pre-trained model MobileNet for Single Shot Multi-Box Detector (SSD). This algorithm is used for real-time detection, and for webcam feed to detect the purpose webcam which detects the object in a video stream. Therefore, we use an object detection module that can detect what is in the video stream. In order to implement the module, we combine the MobileNet and the SSD framework for a fast and efficient deep learning-based method of object detection. The main purpose of our research is to elaborate the accuracy of an object detection method SSD and the importance of pre-trained deep learning model MobileNet. The experimental results show that the Average Precision (AP) of the algorithm to detect different classes as car, person and chair is 99.76%, 97.76% and 71.07%, respectively. This improves the accuracy of behavior detection at a processing speed which is required for the real-time detection and the requirements of daily monitoring indoor and outdoor.