Research on Multi object Detection in Foggy Sky Based on Fog Mod YOLOV8n

Jiangfeng Sun; Xilong Zhang; Yanyang Zeng

doi:10.54097/d02s1503

Authors

Jiangfeng Sun
Xilong Zhang
Yanyang Zeng

DOI:

https://doi.org/10.54097/d02s1503

Keywords:

Fog Environment, Multi-Object Detection, Transformer, Retinexformer, Dehazing, Image Enhancement, Perceptual Loss Function.

Abstract

With the rapid development of smart cities and intelligent transportation, the level of informatization and intelligence in traffic systems is continuously improving. Studies have shown that the accident rate in foggy conditions is more than 30% higher than under normal weather. Fog significantly affects drivers' visibility, reducing safety and increasing driving risks. To address this issue, this paper proposes an improved Transformer-based model, Retinexformer, aimed at enhancing multi-object detection performance in foggy environments. First, to enhance the model's adaptability in complex environments, the Retinexformer model is optimized by incorporating a multi-branch Transformer module. This module enables the model to handle diverse input conditions and process information from different types of scenes more effectively. In addition, an adaptive parameter estimation module is designed to automatically adjust dehazing and image enhancement strategies based on the image’s brightness and contrast. By dynamically setting different dehazing parameters, this module significantly improves the robustness of Retinexformer under varying foggy conditions.In the model's Decoder section, a multi-level feature enhancement module is introduced. After the IGT (Image Gradient Transformation) output, this module fuses multi-scale features, with a particular focus on enhancing details and small object detection capabilities, ensuring accurate identification of targets such as pedestrians and vehicles.The experimental findings indicate that the improved Retinexformer model demonstrates strong robustness and superior detection accuracy in foggy multi-object detection tasks, significantly enhancing traffic safety in foggy environments.

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