ArticleSensors (Basel, Switzerland)2023
YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Computer Vision Applied to the Analysis of Pig Behavior Patterns in an Air-Conditioned Environment.Animals : an open access journal from MDPI · 2026Article
- YOLOv8s-WAMNet: enhancing robust vehicle detection under adverse weather via hybrid attention and multi-scale fusion in real time.Scientific reports · 2026Article
- Road Agglomerate Fog Detection Method Based on the Fusion of SURF and Optical Flow Characteristics from UAV Perspective.Entropy (Basel, Switzerland) · 2025Article
- A Cross-Modal Attention-Driven Multi-Sensor Fusion Method for Semantic Segmentation of Point Clouds.Sensors (Basel, Switzerland) · 2025Article
- IDDNet: Infrared Object Detection Network Based on Multi-Scale Fusion Dehazing.Sensors (Basel, Switzerland) · 2025Article
- Self-Enhanced Mixed Attention Network for Three-Modal Images Few-Shot Semantic Segmentation.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To address this issue, this study proposes a foggy weather detection method based on the YOLOv5s framework, named YOLOv5s-Fog. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer called SwinFocus. Additionally, the decoupled head is incorporated into the model, and the conventional non-maximum suppression method is replaced with Soft-NMS. The experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model, YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.
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