Deep convolutional networks are prominently used in object detection tasks due to their notable performances. These networks typically have pooling layers following the convolution, which effectively subsamples the convolution output, potentially introducing aliasing. An aliased signal emerging in the earlier layers inevitably propagates throughout the network and such distortion potentially prevents getting the best performance out of a network. In this study, we propose integrating the cycle-spinning (CS) into the convolutional layer to have a more robust object detection pipeline. CS makes use of the shift-variant characteristics of pooling, where each shifted version of the image results in a different output. The CS combines these outputs after unshifting them to remove artifacts and alleviate distortion. The proposed method does not introduce any additional trainable parameters and can be straightforwardly integrated into convolution layers. The experimental results show that integrating CS into object detection algorithms in the DOTA dataset results in an up to 5.5% increase in the mAP values.