FriendNet integrates image dehazing and object detection to enhance detection performance in adverse weather conditions.
תקציר
FriendNet proposes a unified framework that combines image dehazing and object detection to improve detection accuracy under degraded conditions. The method leverages detection guidance and task-driven learning to optimize the dehazing network for better detection results. By integrating physics-based priors and attention mechanisms, FriendNet achieves superior performance in both image quality and object detection precision.
"FriendNet uniquely emphasizes the enhancement of both restoration quality and detection accuracy."
"Extensive experiments demonstrate the superiority of our method over state-of-the-art methods on both image quality and detection precision."