In order to address the problem that existing image dehazing algorithms cannot simultaneously consider both dehazing effects and real-time performance when processing road traffic images, a fast all-in-one dehazing network (AOD-Net) algorithm is improved in this study. Firstly, SE channel attention is added to the AOD-Net to adaptively allocate channel weights and focus on important features. Secondly, a pyramid pooling module is introduced to enlarge the receptive field of the network and fuse the features in different scales, so as to better capture image information. Finally, a composite loss function is used to simultaneously focus on image pixel information and structural texture information. Experimental results show that the improved AOD-Net algorithm increases the peak signal-to-noise ratio (SNR) of road traffic images by 2.52 dB after dehazing, and the structural similarity reaches 91.2%. The algorithm complexity and dehazing time are slightly increased, but still meet real-time requirements.