Single Shot Detector

Single-shot detectors (SSDs) are a class of object detection algorithms aiming for fast and efficient object localization and classification within a single network pass. Current research focuses on improving SSD performance in challenging scenarios, such as handling small or occluded objects, adapting to new domains without source data, and enhancing robustness against adversarial attacks. Prominent architectures like YOLO and SSD are being refined through techniques such as spatial transformer networks, multi-view feature fusion, and knowledge distillation, leading to improved accuracy and speed across diverse applications including autonomous driving, medical image analysis, and industrial automation.