This study proposes a bridge crack image segmentation algorithm based on modified PSPNet to resolve the problems such as the low detection accuracy of the traditional bridge crack detection algorithms, loss of details in crack images and discontinuous findings of the existing mainstream semantic segmentation algorithms. First, the bridge images are acquired using an unmanned aerial vehicle, and the bridge crack datasets are procured via image enhancement processing. Second, the crack features are initially extracted using the residual network with dilated convolution. Then, the extracted features are sent to the serial structure of the spatial position self-attention module (SPAM) and pyramid pooling module, enabling the features to achieve rich contextual information in spatial dimensions. The experimental results reveal that the proposed algorithm obtains more precise crack details compared with the existing mainstream semantic segmentation algorithms, with each segmentation index being greatly improved, reaching 84.31% on mean intersection over the union. The proposed algorithm can extract small bridge cracks accurately and completely.