To solve the problems of low accuracy, high false detection rate and missed detection rate of current target detection methods in concrete bridge apparent disease detection, an improved YOLOv5s method on bridge apparent disease detection is proposed. Firstly, to achieve more effective fusion of features at different scales and increase receptive fields, an improved spatial pyramid pooling module is added to YOLOv5s network to enhance feature extraction capability and reduce computational cost. In addition, aiming at the problem of high false detection rate and missed detection rate caused by cross distribution of different defect features in disease images, a light attention module is added in YOLOv5s network. At last, the loss function considering vector angle is adopted to solve the problems caused by the different size of bridge defect, classification difficulty and small data set leading to the boundary box regression mismatch. Experimental results show that the improved YOLOv5s detector can effectively improve the accuracy, reduce the false detection rate and loss detection rate in bridge apparent disease detection task.