ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions

Adverse weather conditions, particularly fog, pose a significant challenge to autonomous vehicles, surveillance systems, and other safety-critical applications by severely degrading visual information. We introduce ADAM-Dehaze, an adaptive, density-aware dehazing framework that jointly optimizes image restoration and object detection under varying fog intensities. First, a lightweight Haze Density Estimation Network (HDEN) classifies each input as light, medium, or heavy fog. Based on this score, the system dynamically routes the image through one of three CORUN branches—Light, Medium, or Complex—each tailored to its haze regime. A novel adaptive loss then balances physical-model coherence and perceptual fidelity, ensuring both accurate defogging and preservation of fine details. On Cityscapes and the real-world RTTS benchmark, ADAM-Dehaze boosts PSNR by up to 2.1 dB, reduces FADE by 30%, and improves object detection mAP by up to 13 points, all while cutting inference time by 20%. These results demonstrate the necessity of intensity-specific processing and seamless integration with downstream vision tasks for robust performance in foggy weather conditions.