Abstract: Moving target shadow detection is a challenging task in the field of video synthetic aperture radar (Video SAR) in recent years. The moving target shadows are characterized by variable sizes and different depths, which make the detection of moving targets in video SAR more difficult. This paper proposes a method for video SAR moving target detection based on an improved neural network, Efficient-Det. Firstly, to obtain more spatial location information of the moving target shadows, the backbone of the Efficient-Det network is reconstructed by incorporating a coordinate attention mechanism. Secondly, to further reduce the computational cost, the neck network is pruned, and depthwise separable convolutions replace the original convolutions in the head network. During the training process, non-maximum suppression is used to select prediction boxes, and the loss function is a combination of regression loss and cross-entropy loss, where the cross-entropy loss is used to mitigate the negative effects of extremely imbalanced positive and negative samples. Experimental validation shows that this method effectively improves the accuracy and efficiency of video SAR moving target shadow detection.