Self-Supervised Feature Contrastive Learning for Small Weak Object Detection in Remote Sensing
Year: 2025
Type: article
Abstract: Despite advances in remote sensing object detection, accurately identifying small, weak objects remains challenging. Their limited pixel representation often fails to capture distinctive features, making them susceptible to environmental interference. Current detectors frequently miss these subtle feature variations. To address these challenges, we propose FCDet, a feature contrast-based detector for small, weak objects. Our approach introduces: (1) a spatial-guided feature upsampler (SGFU) that aligns features by adaptive sampling based on spatial distribution, thus achieving fine-grained alignment during feature aggregation; (2) a feature contrast head (FCH) that projects GT and RoI features into an embedding space for discriminative learning; and (3) an instance-controlled label assignment (ICLA) strategy that optimizes sample selection for feature contrastive learning. We conduct comprehensive experiments on challenging datasets, with the proposed method achieving 73.89% mAP on DIOR, 95.04% mAP on NWPU VHR-10, and 26.4% AP on AI-TOD, demonstrating its effectiveness and superior performance.
Source: Remote Sensing
DOI: 10.3390/rs17081438
Authors: Zheng Li, Xueyan Hu, Jin Qian, Tianqi Zhao, Dongdong Xu, Yongcheng Wang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4409546112