Abstract: To solve the problem of low accuracy and high false alarm rate of synthetic aperture radar (SAR) nearshore area vessel detection, a new SAR nearshore area vessel detection method based on improved attention mechanism and rotating frame is proposed. Firstly, the feature extraction capability of the network was enhanced by improving the coordinate attention mechanism and introducing it into the feature extraction network. Secondly, the angle classification head was added and the two-dimensional Gaussian distribution was introduced to calculate the KL divergence between the prediction distribution and the target distribution, so as to evaluate the loss value of the rotating frame and complete the angle information extraction of the target. Then, based on the anchor frameless (AF) mechanism of YOLOX algorithm, the model can be made lightweight and the positioning accuracy can be further improved by reducing the redundancy of candidate frames. Finally, the model was tested on the open dataset Offical - SSDD, and the inference verification was performed on the embedded platform (NVIDIA Jetson AGX Xavier). The calculation parameter of the algorithm model is only 1.14M, and the average detection accuracy of the algorithm model is 18.77%, higher than that of the YOLOX model in the nearshore condition, and the overall detection accuracy reaches 94.2%. The verification results show that the algorithm is suitable for dense ship target detection in any direction in complex scenes and can meet the requirements of real-time processing.