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 ₒ, outperforming the strongest AP baseline by + 1. 2 AP and the strongest AP ₒ baseline by +1. 1 AP ₒ. Comprehensive experiments on multiple benchmarks demonstrate that NIB-PGD achieves state-of-the-art performance in detection accuracy, robustness, and generalization.
Zhou et al. (Thu,) studied this question.