An improved deep learning-based optimal object detection system from images

Abstract

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

“…As passive techniques are not dependent on pre-embedded data, such as watermarks or signatures, it is still possible to check a video's integrity and authenticity without using pre-embedded data. The researcher faces a challenge when he is unaware of the embedded information in the video when using passive techniques [1]. In recent years, the scientific community has increasingly focused on passive techniques to detect video forgeries.…”