DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection
PositiveArtificial Intelligence
- A novel framework called DMAT has been proposed to address the challenges posed by atmospheric turbulence (AT) on surveillance imagery, which affects both visualization quality and object detection accuracy. This end-to-end training strategy aims to compensate for distorted features while enhancing both visualization and object detection capabilities.
- The development of DMAT is significant as it leverages deep learning techniques to improve the performance of object detection systems under conditions of atmospheric distortion, potentially leading to advancements in surveillance technology and applications in various fields.
- This innovation reflects a broader trend in artificial intelligence where deep learning is increasingly utilized to overcome limitations in traditional imaging and detection methods, highlighting the ongoing efforts to enhance the reliability and effectiveness of machine learning applications across diverse environments.
— via World Pulse Now AI Editorial System