CNN Based Missing Object Detection

Abstract

Abstract: Missing object detection is an important problem in computer vision with applications in various fields such as autonomous driving, surveillance, and robotics. Deep neural networks, particularly convolutional neural networks (CNNs), have shown promising results in addressing this problem. In this literature survey, we review recent research papers that focus on using CNNs for missing object detection. We analyze the different approaches and techniques employed by these papers, including context-aware detection, generative adversarial networks, multi-task learning, and transfer learning. We also discuss the challenges and limitations of these approaches and suggest possible directions for future research. Overall, the literature survey highlights the potential of CNNs in addressing the missing object detection problem and provides a comprehensive understanding of the recent advancements in this field.