# Position Detection of Doors and Windows Based on DSPP-YOLO
## 3 Dspp-Yolo
### Pseudocode
Compared with the original YOLOV3 algorithm, the detection accuracy is improved by 3.3%, 8.8% and 6.05% respectively. And with the same amount of test data set being tested, although some Res-n modules are replaced by Des-n modules. However, the algorithm of DSPP-YOLO algorithm is only about 2 s slower than that of YOLOV3 algorithm, because it still keeps the hop connection in the deep network structure and reduces the influence on the detection speed. Compared with other algorithms in the same series, YOLOV1 algorithm and YOLOV2 algorithm are the earlier and simpler models of YOLO algorithm. However, YOLOV3-tiny is a variant of YOLOV3 with a simple network structure, which only contains 23 layers of network and two output scales, so its detection speed is the fastest, only 5.34 s, compared with DSPP-YOLO, the detection precision of door is only 73.9%, and that of window is reduced to 27.5%. The YOLOV3-144884 algorithm extends Darknet53 network by adding a Res-4 module and a lower sampling layer, the Res-2 module of the original YOLOV3 is extended to Res-4, but it can be seen from the experimental results that the increase of the network depth leads to the instability of the structure, which leads to the decrease of the detection precision, therefore, the superiority of DSPP-YOLO algorithm can be verified. The target detection results of the YOLOV3 algorithm and the DSPP-YOLO algorithm are shown in Figures 7 and 8. The YOLOV3 algorithm has a big problem in the detection of the public image part of the data set. Small windows are overlooked, or the detection frame of the door contains other background distractors. However, DSPP-YOLO can improve this situation, correcting the small target that was missed in the original network--the remote window, and the misidentification--taking the entire image as the door and actually being the window, but since the window is not relatively fixed characteristics, so there will still be false detections and missed detections. Figure 8: DSPP-YOLO test results. ## 5 Conclusions

Based on the YOLOV3 algorithm, this paper proposes an improved algorithm model for the detection of doors and windows by autonomous mobile robots in unknown environments-DSPP-YOLO. Firstly, we introduce SPP module to solve the problem of large sample size gap and inconsistent receiving domain scale. In order to better distinguish the semantic information of special types of doors and windows, we add a down sampled layer to deepen the learning of semantic features. Then, to solve the problem that the position information of the remote window may be lost in the deep network, we combined the idea of DenseNet and replaced the first four Res-n modules in the YOLOV3 algorithm with Des-n modules. Finally, the K-means++ algorithm is used to re-cluster the anchor size to reduce the detection error caused by the candidate frame. YOLOV3 algorithm is a typical one-stage object detection algorithm, which can be improved from the following two aspects in the future: (1) We can build a backbone network with stronger representation ability to improve the accuracy of the algorithm; (2) We can propose a new loss function to solve the problem of sample imbalance encountered in the process of object detection. In the future, we plan to apply the doors and windows object detection in the autonomous exploration algorithm of autonomous mobile robots in unknown environments. Writing--Review & Editing, T.Z. ; Writing--Original Draft Preparation, J.L. ; Validation, Y.J. ; Methodology, M.Z. ; Project administration, M.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) (61603297) and Natural Science Foundation of Shanxi Province (2020)Q-219. The paper does not involve any ethical guidelines. The authors declare no conflict of interest. ## References

* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Progress and Applications of Visual SLAM. _Acta Geod. Et Cartogr. Sin._**2018**, _47_, 770-779.