ThumbDet: One thumbnail image is enough for object detection

•A novel Transformer-based architecture for very low resolution images object detection is proposed.•An image down-sampling module that utilizes the feature extraction capabilities of CNNs to generate thumbnail images from the original ones.•A distillation-boost supervision strategy is introduced to maintain the detection performance of thumbnail images as the original-size inputs.•Our method achieves a satisfactory detection performance (i.e. 32.3% in mAP) while drastically reduce computation and memory requirements (speed up 1.26×).•The performance of our method outperforms bicubic method by a large margin (3.2% in mAP) and achieves comparable results compared to SOTA method on COCO dataset.