To solve the problems of low detection accuracy, high false detection rate, and high missed detection rate in bridge apparent disease detection based on target detection method, a recognition method with high accuracy of bridge apparent disease detection based on improved YOLOv3 is proposed. A pooling module with a fixed block size is added to the detection layer of YOLOv3 to realize effective fusion of local features and global features. To enhance the transmission and utilization efficiency of bridge disease features in the network and improve the detection efficiency, a DenseNet dense connection network structure is introduced in the feature extraction network of YOLOv3. To deal with the insufficient number of samples in the existing bridge disease data set, data enhancement technology is used to expand the sample images. The experimental results show that the mean accuracy precision (mAP) of the improved YOLOv3 on bridge apparent disease detection is increased by 3.0% and the model training time decreased by 33.2%, with a reduced false detection rate and a lower missed detection rate.