## 5 Conclusions  
In this study, a new detector, YOLO-DSD, based on YOLOv4, was proposed to balance the accuracy, deployability and inference time for remote sensing object detection. Three main improvements were utilized in YOLO-DSD, including the DenseRes Block, S-CBL\(\times\)5 and DCA Block. Firstly, the DenseRes Block improves the backbone, which can better compress and extract the object feature with a high accuracy but less computational consumption. Secondly, S-CBL\(\times\)5 introduced in the neck can mitigate feature loss without increasing the consumption and inference time. Finally, a new channel attention mechanism, the DCA Block, added to S-CBL\(\times\)5 better highlights the important features in the channel dimension. Experiments on a large dataset, DIOR, were conducted to analyze the detection performance from the accuracy (mAP), deployability (Params and Flops) and speed (FPS). The results of the experiments indicate that the proposed DenseRes Block is superior to other feature extraction modules, such as the Res Block, ResNeXt Block, Res2 Block, Dense Block and CSP Block. Moreover, S-CBL\(\times\)5 performs better than currently widely used FPN, BFPN and PANet. In addition, the proposed DCA Block outperforms other attention mechanisms, including the SE Block, ECA Block, CA Block and CBAM Block. Compared with YOLOv4, YOLO-DSD reduces Params by 23.9% and Flops by 29.7%, but increases FPS by 50.2%, while mAP\({}^{0.5}\) and mAP\({}^{0.50.95}\) increased from 71.3% to 73.0% and 39.1% to 40.0%, respectively. Compared with other SOTA detectors, including Faster-RCNN, SSD, RetinaNet, YOLOv3, YOLOv4, CenterNet, YOLO-Lite and EfficientDet, YOLO-DSD achieves the optimal balance of accuracy, deployability and inference time. In terms of the RSOD dataset, compared with YOLOv4, YOLO-DSD achieves 1.5-2.6%, 0.8-1.2% and 50.2-59.3% increases in mAP\({}^{0.5}\), mAP\({}^{0.50.95}\) and FPS under different input sizes, including 416 \(\times\) 416, 512 \(\times\) 512 and 608 \(\times\) 608. However, YOLO-DSD has a limitation in processing a serious inter-class similarity, such as 'bridge' and 'overpass', compared with YOLOv4. In order to further improve the performance of the proposed detector, we will try to combine depthwise separable convolution [52] with the proposed DenseRes Block for a better feature extraction and deployability reduction. Moreover, other non-consumption methods, such as image preprocessing and anchor optimization, will be considered to improve the detector. Not applicable. All data used during the study have been uploaded at: [[https://gchengr-nwpu.github.io/#Datasets](https://gchengr-nwpu.github.io/#Datasets)]([https://gchengr-nwpu.github.io/#Datasets](https://gchengr-nwpu.github.io/#Datasets)) (last accessed on 27 July 2022) and [[https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-)]([https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-)) (last accessed on 27 July 2022). We gratefully appreciate the editor and anonymous reviewers for their efforts and constructive comments, which have greatly improved the technical quality and presentation of this study. The authors declare no conflict of interest. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Design Guidelines for Cooperative UAV-supported Services and Applications. _ACM Comput. Surv._**2022**, _54_, 1-35. [CrossRef]
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