Active Learning System for Autonomous Vehicle Object Detection

This study focuses on object detection, a key area in deep learning that addresses the issue of training models when there is a scarcity of labeled data. This is particularly relevant in areas such as autonomous driving, where obtaining labeled data is expensive and time-consuming. One of the primary difficulties in enhancing object detection is the limited quantity of available data and the need for a well-balanced support set. To address these hurdles, we introduce a new framework. This framework is designed to identify and recommend unlabeled data that can be beneficially added to the support set. It uses prior knowledge and active learning strategies to provide recommendations. The effectiveness of our recommendation system is demonstrated by comparing its performance with that of a model trained on randomly selected data. This comparison demonstrates the advantages of the proposed approach in improving object detection.