In this paper, we propose a novel crack detection algorithm based on feature enhanced whole nested network to resolve the issue of inaccurate crack segmentation caused by complex background and changeable texture of concrete cracks in natural scenes. First, based on the holistically-nested network (a deep learning edge detection network), the multi-scale supervision mechanism was adopted to integrate the prediction results of concrete cracks of different scales to enhance the expression ability of the network to the linear topology of concrete cracks. Then, we used a convolution-deconvolution feature fusion module to effectively integrate the deconvolution deep semantic features and convolution shallow detail features of concrete cracks. The deep semantic features can reduce the interference of complex backgrounds and improve the feature response of the fuzzy crack area. The shallow features can improve the expression ability of crack details and the quality of crack features. Finally, we proposed a hybrid void convolution boundary thinning module that used residual network and void convolution group to refine the fracture boundary and improve the accuracy of fracture segmentation. Using the Bridge_Crack_Image_Data dataset and Crack Forest Dataset, the accuracy of the proposed algorithm was 92.1% and 91.6% and the F₁-score was 80.2% and 91.1%, respectively. The experimental results show that the proposed algorithm obtains stable and accurate segmentation results in complex natural environments and attains strong generalizations.