AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments

Abstract

Foggy and hazy weather conditions can significantly reduce the clarity of images captured by cameras, making it difficult for object detection algorithms to accurately recognize targets. This degradation can cause failures in autonomous or assisted driving systems, posing severe safety threats to both drivers and passengers. To address the issue of decreased detection accuracy in foggy weather, we propose an object detection algorithm specifically designed for such environments, named AODs-CLYOLO. To effectively handle images affected by fog, we introduce an image dehazing model, AODs, which is more suitable for detection tasks. This model incorporates a Channel–Pixel (CP) attention mechanism and a new Contrastive Regularization (CR), enhancing the dehazing effect while preserving the integrity of image information. For the detection network component, we propose a learnable Cross-Stage Partial Connection Module (CSPCM++), which is used before the detection head. Alongside this, we integrate the LSKNet selective attention mechanism to improve the extraction of effective features from large objects. Additionally, we apply the FocalGIoU loss function to enhance the model’s performance in scenarios characterized by sample imbalance or a high proportion of difficult samples. Experimental results demonstrate that the AODs-CLYOLO detection algorithm achieves up to a 10.1% improvement in the mAP (0.5:0.95) metric compared to the baseline model YOLOv5s.