Data Augmentation via Bit-Plane Manipulation for Object Detection

Current object detection algorithms based on deep learning- heavily depend on a substantial amount of annotated data for model training. High-quality datasets are crucial in addressing challenges such as overfitting. However, collecting large amount of annotated data poses challenging in certain fields. To mitigate this limitation, this paper introduces a data augmentation method based on low-bit plane manipulation. Specifically, this paper employs selected data augmentation methods by processing the low bit planes of the annotated regions in images. This can modify the low-frequency information of the images while minimizing significant visual changes. It is crucial for tasks that depend on high-quality image. During the bit-plane combination process, the augmented image data is achieved through the combination of different bit planes, thereby increasing the diversity of training data. The effectiveness of the proposed method is validated on existing object detection and classification methods, demonstrating notable performance improvements on public datasets, voc2007, voc2012, and kitti2D. These results demonstrating its applicability to object detection and classification that require high-quality input images, enhancing the performance of the algorithms. The code and data can be find here: https: github.com cjjhf Data_augmentation.