Owing to the fault and leak detection problems caused by complex scenes and diverse scales in remote sensing image ship detection, a lightweight ship classification detection method based on improved YOLOv5s is proposed herein to realize real-time rapid ship classification and detection despite limited equipment computing capability. This method applies a lightweight and efficient channel attention technique to the backbone feature extraction network to obtain a novel feature extraction network with an improved ability to identify ships in complex remote sensing images. The feature maps with different levels obtained from the feature extraction network were input into the weighted bidirectional feature pyramid structure to optimize the fusion of high and low stage features of the backbone network, and experiments were conducted on the ship dataset of remote sensing images. The results show that the mean average precision of the improved network model has increased from 83.9% to 89.2% and the average precision for detecting aircraft carriers, warships, civil ships, and submarines has increased by 1.6 percentage points, 0.9 percentage points, 8.8 percentage points, and 9.5 percentage points, respectively. Additionally, the average detection speed and network complexity are considerably better than the other algorithms.