TY - GEN
T1 - A Fine-Grained Aircraft Detection Method Based on an Asynchronous Push-Pull Network
AU - Ding, Yan
AU - Guo, Shupeng
AU - Xiao, Jianbang
AU - Zhang, Bozhi
AU - Cui, Luheng
AU - Zhao, Minjin
AU - Zhang, Xing
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - With the rapid development of high-resolution remote sensing technologies, remote sensing imagery has become indispensable in military reconnaissance and precision strike missions. However, fine-grained object detection in such imagery remains highly challenging due to subtle inter-class discrepancies and pronounced intra-class variations. To address these challenges, this paper proposes a fine-grained object detection framework designed to optimize detection accuracy. The framework integrates an improved Faster R-CNN with a multi-level feature-enhanced cascaded RoI head, an asynchronous push-pull network, and a contrastive learning-based optimization module. Experimental evaluations conducted on the FAIR1M dataset demonstrate that the proposed model outperforms the baseline by 10.8 percentage points in mAP@50, thereby achieving a notable enhancement in fine-grained detection accuracy.
AB - With the rapid development of high-resolution remote sensing technologies, remote sensing imagery has become indispensable in military reconnaissance and precision strike missions. However, fine-grained object detection in such imagery remains highly challenging due to subtle inter-class discrepancies and pronounced intra-class variations. To address these challenges, this paper proposes a fine-grained object detection framework designed to optimize detection accuracy. The framework integrates an improved Faster R-CNN with a multi-level feature-enhanced cascaded RoI head, an asynchronous push-pull network, and a contrastive learning-based optimization module. Experimental evaluations conducted on the FAIR1M dataset demonstrate that the proposed model outperforms the baseline by 10.8 percentage points in mAP@50, thereby achieving a notable enhancement in fine-grained detection accuracy.
KW - Aircraft detection
KW - Fine-grained recognition
KW - Remote sensing imagery
UR - https://www.scopus.com/pages/publications/105033592150
U2 - 10.1109/ICACR68388.2025.11360068
DO - 10.1109/ICACR68388.2025.11360068
M3 - Conference contribution
AN - SCOPUS:105033592150
T3 - 2025 9th International Conference on Automation, Control and Robotics, ICACR 2025
SP - 113
EP - 121
BT - 2025 9th International Conference on Automation, Control and Robotics, ICACR 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Automation, Control and Robotics, ICACR 2025
Y2 - 28 November 2025 through 30 November 2025
ER -