Node Reservation Based Incremental Learning Network for Object Detection

Object categorization is a crucial element in AI-driven computer vision systems, with its influence spanning from advanced surveillance technologies to basic projects. This field faces a key challenge in strategically incorporating new objects into existing networks, balancing between efficient training and precise object detection. Our paper introduces an innovative two-step method to fluidly integrate new objects into current networks, maintaining the accuracy of existing object detection accuracy and the integrity of prior knowledge. This method relies on the network's foresight in predicting upcoming class additions during the initial training, allocating specific layers for future expansions. These layers are then selectively activated when new classes are added, simplifying retraining processes and preserving pre-existing knowledge. This approach is particularly beneficial for time-sensitive and resource-limited systems, offering proactive resource allocation and adaptability to new class introductions.