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<p>Object detection on roadways is crucial for autonomous driving and advanced driver assistance systems. However, adverse weather conditions, notably rain, significantly degrade the performance of these systems. This paper presents a novel approach to improve the detection of road objects in rainy weather scenarios by applying YOLOv11 model. This includes specialized data augmentation techniques to simulate rainy conditions, adjustments in network architecture to improve resilience to rain-induced noise, and optimized training strategies to enhance model performance. The study leverages BDD100K, Cityscapes, and DAWN-Rainy datasets of various road scenarios under different rain intensities. We systematically augment these datasets to ensure the model learns to identify objects obscured by rain streaks and reflections. Such enhancements enable better handling of occlusions and reduced visibility in the feature extraction layers. Also, to ensure the model’s efficiency and suitability for real-time applications, we apply a network pruning technique, which reduces the model size and computational requirements without sacrificing performance. Extensive experiments demonstrate that our model has a comparable mean Average Precision with the baseline YOLOv11 but at a 2x compression ratio under rainy conditions. This research contributes to the field of autonomous driving by providing a more reliable object detection system for adverse weather conditions, improving overall road safety.</p>

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Improved YOLOv11 with weights pruning for road object detection in rainy environment

  • 期刊论文

  • 发表日期：

    2025年4月7日

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  • Navjot Singh,
  • Chandra Prakash Maurya,
  • Bharat Mahaur,
  • Satish Kumar Singh

摘要

Object detection on roadways is crucial for autonomous driving and advanced driver assistance systems. However, adverse weather conditions, notably rain, significantly degrade the performance of these systems. This paper presents a novel approach to improve the detection of road objects in rainy weather scenarios by applying YOLOv11 model. This includes specialized data augmentation techniques to simulate rainy conditions, adjustments in network architecture to improve resilience to rain-induced noise, and optimized training strategies to enhance model performance. The study leverages BDD100K, Cityscapes, and DAWN-Rainy datasets of various road scenarios under different rain intensities. We systematically augment these datasets to ensure the model learns to identify objects obscured by rain streaks and reflections. Such enhancements enable better handling of occlusions and reduced visibility in the feature extraction layers. Also, to ensure the model’s efficiency and suitability for real-time applications, we apply a network pruning technique, which reduces the model size and computational requirements without sacrificing performance. Extensive experiments demonstrate that our model has a comparable mean Average Precision with the baseline YOLOv11 but at a 2x compression ratio under rainy conditions. This research contributes to the field of autonomous driving by providing a more reliable object detection system for adverse weather conditions, improving overall road safety.