Physically Guided Diffusion Framework With Neural Information Bottleneck Regulation for Robust Small Object Detection

Robust small object detection in adverse environments remains challenging due to physical degradations and unstable feature representations. Existing detectors often struggle to maintain semantic consistency in haze, rain, and low-light conditions, leading to degraded accuracy and poor generalization. To address these issues, we propose a unified neural information bottleneck with physics-guided diffusion (NIB-PGD) framework. The proposed framework couples physical priors with information-theoretic regularization in a dual-diffusion paradigm, enabling robust feature encoding and noise-resilient object localization. A multiscale enhance-fuse-context attention (EFC-A) encoder captures hierarchical semantics and contextual dependencies, while a physics-guided feature-level diffusion injects realistic degradation kernels into the noise schedule to enhance robustness. In parallel, a box-level diffusion reformulates detection as iterative denoising, progressively refining bounding boxes without anchors or queries. A second-order information bottleneck (2O-IB) constraint dynamically regulates mutual information to suppress redundancy and preserve task-relevant semantics. On our composite benchmark (constructed from BDD100K, SODA-D, and an in-house small object set), NIB-PGD achieves 52.3% AP and 35.7% AP ${}_{s}$ , outperforming the strongest AP baseline by + 1.2 AP and the strongest AP ${}_{s}$ baseline by +1.1 AP ${}_{s}$ . Comprehensive experiments on multiple benchmarks demonstrate that NIB-PGD achieves state-of-the-art performance in detection accuracy, robustness, and generalization.