Multiclass Object detection And Counting Using Classification

Computer Enginerring

This paper designed how to recognize and count objects in a real time manner in a highspeed inspection environment with large volumes of data, so as to verify the concept (smart camera with GPU cores) we proposed. A wide, active, and challenging field of computer vision is real-time object detection. Real-time object detection and counting is a vast. vibrant yet inconclusive area of computer vision. Image localization refers to the process of finding a single object in an image, while object detection refers to the process of finding several objects in an image. This recognizes a class of semantic items in digital photos and movies. Smart camera is equipped with processor, memory, communication interface and operating system, so it can process large amounts of data in advance to assist follow-up automatic inspection and judgment. Real-time object detection has a variety of uses, including object tracking, video surveillance, people counting, pedestrian detection, selfdriving automobiles, facial recognition, sports ball tracking, and more. When detecting objects with the aid of OpenCV a library of programming functions primarily geared toward real-time computer vision, Convolution Neural Networks is a representative technique of deep learning.

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