Ship targets in remote sensing images have multi-scale characteristics, changeable backgrounds, and complex meteorological characteristics, which lead to low accuracy, false detection, and missed detection of small target ships. In response to the above situation, an improved small-target ship detection model based on YOLOv5s was proposed. First, in order to solve the problems of scale changes and background variability in ship detection, the ASFF(adaptive spatial feature fusion) module was introduced. Secondly, in order to reduce the calculation amount and parameter amount of the detection network, the BoTNet attention mechanism was introduced, and then in order to improve the overall network to improve the detection accuracy, the EIoU border loss function was used, and finally the Slim-neck network was introduced to ensure the overall lightweight of the network. Experiments show that on the main data set LEVIR-Ship, compared with the benchmark YOLOv5s, mAP@0.5 increased by 7.1% to 81.3%, the number of parameters is reduced by 0.44 M, the calculation amount is reduced by 0.6GFLOPs, and the weight was reduced by 0.9 M. The proposed method performs better in various key indicators and achieves high-precision small target ship detection in complex environments. Comparative experiments are conducted on the verification data set McShips. The experiments show that the proposed method still performs better, verifying the universal applicability of the proposed method.