Robust Object Detection

Robust object detection aims to create computer vision systems that accurately identify objects in images and videos even under challenging conditions, such as poor lighting, adverse weather, or adversarial attacks. Current research focuses on improving model robustness through techniques like data augmentation with synthetic perturbations, multi-modal fusion (combining data from different sensors), and the development of novel architectures and algorithms, including those based on denoising diffusion processes and adversarial training. These advancements are crucial for deploying reliable object detection in safety-critical applications like autonomous driving and security systems, where performance under real-world variability is paramount.