Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback
This addresses the time-consuming and labor-intensive process of dataset collection for computer vision researchers, but it appears incremental as it builds on prior efforts to reduce annotation effort.
The paper tackles the problem of reducing manual effort in collecting and annotating images for training 2D object detection systems by proposing a human-in-the-loop web application with online visual feedback features. The results suggest feasibility for annotation and object detection, though no concrete numbers are provided.
Training deep-learning-based vision systems require the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies have attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this, we propose a human-in-the-loop dataset collection method that uses a web application. To counterbalance the workload and performance by encouraging the collection of multi-view object image datasets in an enjoyable manner, thereby amplifying motivation, we propose three types of online visual feedback features to track the progress of the collection status. Our experiments thoroughly investigated the impact of each feature on collection performance and quality of operation. The results suggested the feasibility of annotation and object detection.