Foggy Target Detection Algorithm Based on CBAM-FE and SPD-Conv

Addressing the issues of decreased accuracy and detection failures caused by blurred edges of target objects and reduced image resolution under foggy conditions, a vehicle and pedestrian target detection algorithm combining SPD-Conv structure and lightweight CBAM-FE attention mechanism was proposed. Firstly, the CBAM-FE attention mechanism was introduced into the backbone network to reduce fog interference and enhance foggy edges through local standard deviation pooling and learnable edge detection. Secondly, SPD-Conv is employed to minimize the loss of critical information caused by downsampling and to improve the effect of distant target detection and multi-scale feature fusion. The experimental evaluation reveals that our novel methodology attains the mAP of 76.4% on the RTTS dataset and 44.5% on the Foggy Cityscapes dataset, which is 1.6% and 1.2% higher than those of the original model. Experimental results demonstrate that the proposed algorithm enhances detection precision without compromising its real-time processing capabilities.