Harmful Object Detection Using Deep Learning
Keywords:
Video Processing, Image, Classification, Computer VisionAbstract
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 Mobile-Net 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, a module to track the objects in the video stream is used. We combine the Mobile-Net and the SSD system to deploy the module in a rapid and effective, thorough learning method of object detection. Our research focuses on improving the precision of the SSD object detection procedure and on the importance of Mobile-Net's pre-trained deep learning model. This increases the accuracy of conduct identification at a speed necessary for real-time detection and the standards of indoor and outdoor day-to-day surveillance.
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