Timely and accurately capturing the tiny cracks in the shaft lining is of great significance for shaft safety. Lightweight detection models are the key to realizing the automatic detection of shaft lining cracks. Departing from existing traditional methods that focus on extracting deep semantic information， the application of geometric structure information represented by shallow features should be paid attention to and a lightweight detection model E-YOLOv5s for shaft lining cracks is proposed. Firstly， the lightweight convolution module， ECAConv， is designed， which integrates traditional convolution， depth-separable convolution， and an attention mechanism called ECA. Then， thefeature extraction capabilities are further enhanced by incorporating skip connections to construct the feature comprehensive extraction unit， E-C3. Thereby， the backbone network ECSP-Darknet53 is obtained， which significantly reduces network parameters and enhances the ability to extract deep fracture features of cracks. Finally， the feature fusion module ECACSP is proposed and the thin neck feature fusion module E-Neck is built by using multiple groups of ECAConv and ECACSP modules. The purpose of E-Neck is to fully fuse the geometric information of small crack targets and the semantic information of crack cracking degrees while accelerating the network reasoning. Experimental results show that the detection accuracy of E-YOLOv5s on the self-made shaft lining dataset is improved by 3.3% compared to YOLOv5s while the number of model parameters and GFLOPs are reduced by 44.9% and 43.7%， respectively. E-YOLOv5s can help promote the application of automatic detection of shaft lining cracks.