The aircraft detection in Synthetic Aperture Radar (SAR) images encounters several challenges including complex backgrounds，dimand small-scaleaircraft targets，big differences in targets under different imaging conditions，and fragmented target structures. To solve the problems，a novel aircraft target detection algorithm named Aircraft Target Detection Model(ATDM) for SAR images is proposed to improve the detection accuracy of aircraft targets in SAR images in complex backgrounds. Taking YOLOv8s as the baseline model，the algorithm includes three key modules，namely，the Convolutional Block Attention Module (CBAM)，Omni-Dimensional Feature Extraction (ODFE) module，and Deformable Global Feature Fusion (DGFF) module，along with an improved loss function. In order to improve the feature extraction ability of the network in complex backgrounds，the CBAM is integrated into the backbone of the baseline network to capture aircraft target features across spatial and channel dimensions. The ODFE utilizes the dynamics of four dimensions of convolution kernel space，namely，the size of the kernel，the number of input channels，the number of output channels and the number of convolution kernels，to extract features from different types of aircraft targets across the four dimensions by using the parallel operation strategy，thereby enhancing the detection of aircraft targets，especially small targets with weak scattering characteristics in complex backgrounds. The DGFF dynamically adjusts the shapes and sizes of convolution kernels to accommodate variations in the imaging conditions，thereby facilitating global information feature fusion. Finally，the bounding box regression loss function is improved to be a dynamic non-monotonic focusing loss function WIoU. The dynamic non-monotonic focusing mechanism is adopted，and the outlier degree is used to evaluate the quality of the anchor frame to mitigate mislabeling effects in SAR images. In order to assess the performance of the proposed ATDM，the experiments are conducted on SADD and Gaofen-3 SAR aircraft datasets. The Average Precision (AP) achieved on the two datasets reaches 95.4% and 98.2% respectively. Ablation experiments and comprehensive analysis indicate the efficacy of the proposed three modules and loss function. Furthermore，compared with other target detection algorithms，the proposed algorithm achieves the highest AP.