FTLS: Fragments-Based Twofold Learning Strategy for Target Recognition on the Lunar Surface

Abstract

In lunar exploration missions, tasks such as target recognition or obstacle detection always encounter significant challenges due to the quality of images and the limited availability of training samples. Typically, existing methodologies can only recognize and locate a limited number of targets within simple scenarios, and models developed within simulated environments lack practical applicability. To alleviate above issues, an image fragments-based twofold learning strategy has been conceptualized. This strategy facilitates the decoupling of target features from environmental attributes through processes such as image fragmentation, confidence evaluation, and domain transfer, thereby achieving enhanced recognition accuracy across diverse lighting conditions. To provide more accurate localization and identification of a wider variety of targets, $\mathrm {\omega }$ -CIoU loss is introduced to address the anomalies in prediction box scale and target count induced by flare and shadow features. Moreover, the establishment of the Real Chang’e Lunar Landscape Dataset, the most extensive public dataset for lunar surface target recognition in terms of sample quantity and diversity, provides an invaluable experimental foundation for future research in this field. Comprehensive experimental results on the RCLLD and public dataset ALLD demonstrate that the FTLS can significantly boosts recognition precision and generalization capabilities without escalating model complexity, outperforming prevailing target recognition methodologies for lunar exploration rovers.Note to Practitioners—This research is inspired by the need for high-precision, robust target recognition and avoidance systems for lunar rovers during lunar surface exploration missions. These systems are crucial for deployment on rovers with limited computational resources and energy, providing a convenient platform for future lunar terrain surveys, lunar base site selection, and mineral collection tasks. However, existing target recognition systems usually require training on extensive actual lunar datasets and often suffer from significantly reduced accuracy in areas with severe lighting changes, which decreases reliability on the lunar rover platform—an unacceptable risk for the cost-intensive lunar exploration missions. To address these challenges, this paper proposes a twofold learning strategy based on image fragments that trains on limited actual lunar data, offers resistance to flare and shadow interference, and its effectiveness is validated on both actual lunar and simulated datasets.