PRNet: Original Information Is All You Have
This work addresses the challenge of detecting small objects in aerial imagery, which is critical for applications like surveillance and mapping, but it appears incremental as it builds on existing FPN-based methods with specific enhancements.
The paper tackles the problem of small object detection in aerial images by proposing PRNet, a real-time framework that preserves shallow spatial features to improve detection accuracy, achieving superior performance on datasets like VisDrone, AI-TOD, and UAVDT compared to state-of-the-art methods.
Small object detection in aerial images suffers from severe information degradation during feature extraction due to limited pixel representations, where shallow spatial details fail to align effectively with semantic information, leading to frequent misses and false positives. Existing FPN-based methods attempt to mitigate these losses through post-processing enhancements, but the reconstructed details often deviate from the original image information, impeding their fusion with semantic content. To address this limitation, we propose PRNet, a real-time detection framework that prioritizes the preservation and efficient utilization of primitive shallow spatial features to enhance small object representations. PRNet achieves this via two modules:the Progressive Refinement Neck (PRN) for spatial-semantic alignment through backbone reuse and iterative refinement, and the Enhanced SliceSamp (ESSamp) for preserving shallow information during downsampling via optimized rearrangement and convolution. Extensive experiments on the VisDrone, AI-TOD, and UAVDT datasets demonstrate that PRNet outperforms state-of-the-art methods under comparable computational constraints, achieving superior accuracy-efficiency trade-offs.