GPE-YOLO: a gradient-prior enhanced detector with dynamic sampling for robust object detection in adverse weather

Abstract

Although deep learning-based object detectors have achieved remarkable performance on standard benchmarks, their reliability deteriorates significantly under adverse weather conditions (e.g., rain, snow, and fog) due to severe image degradation and the loss of critical structural information. To address this challenge, this paper proposes GPE-YOLO, a robust detection framework built upon the YOLOv11 architecture that explicitly integrates multiscale edge priors to enhance feature resilience. Specifically, we introduce three novel components: 1) Dual-Stream Gradient-Pooling Stem (DSGPS) serves as an efficient preprocessing unit, utilizing parallel 3D Sobel and spatial convolutions to capture comprehensive low-level features and mitigate noise propagation at the input stage. 2) The Pyramidal Edge Prior Generation Module (PEPGM) extracts multi-scale edge maps from shallow layers and injects them into the backbone network via max pooling, effectively suppressing background interference while preserving structural details. 3) The Dynamic Edge-Aware Feature Pyramid (DE-AFP) incorporates DySample and CSPStage to optimize feature upsampling and downsampling processes, thereby ensuring efficient semantic information transmission and robust multi-scale fusion across the network. Extensive experiments on the VOC2012-CWC dataset demonstrate the superiority of our method. GPE-YOLO achieves an mAP@0.5 of 32.84% and mAP@0.5:0.95 of 24.59%, outperforming the baseline YOLOv11 by substantial margins of 4.85% and 3.86%, respectively. These results validate the potential of GPE-YOLO for reliable deployment in real-world adverse weather scenarios.