Cross-Transfer Learning for Enhancing Object Detection in Remote Sensing Images

Deep learning models have gained widespread acclaim for their effectiveness and precision in object detection systems. However, the development of novel models is often hindered by various challenges, such as limited data availability, scarcity of GPU resources, and the need for specialized knowledge. Despite the adoption of existing transfer learning techniques, these methods have frequently fallen short of achieving the desired results. Consequently, this study introduces the concept of cross-transfer learning (CTL) as an innovative joint learning strategy. CTL integrates curriculum and transfer learning methodologies, along with a difficulty measurer, to leverage knowledge obtained from simpler to more complex datasets across a sequence of subsets for learning new objectives. Data are categorized using a difficulty measurer, which ranks it based on visual recognizability and complexity. Throughout the learning phase, the updated weights from each subset are transferred to the next one, thereby enhancing the model’s capability to extract more robust features. This enhancement significantly improves the model’s generalization and convergence rate. The effectiveness of the proposed method was evaluated by utilizing manually annotated data from three publicly accessible remote sensing datasets. The experimental results demonstrate a significant increase in accuracy when CTL is implemented with YOLOv7, with improvements of 21%, 45%, and 26% on the VEDAI-VISIBLE, VEDAI-IR, and DOTA datasets, respectively.