# Position Detection of Doors and Windows Based on DSPP-YOLO
###### Abstract  
Autonomous exploration of autonomous mobile robots in unknown environments is a hot topic at present. Object detection is an important research direction in improving the autonomous capability of autonomous mobile robots in unknown environments. In object detection, doors and windows have many similar features and are difficult to distinguish. Therefore, improving the detection accuracy of doors and windows is helpful to improve the autonomous ability of autonomous mobile robots. Aiming at the problem of insufficient doors and windows detection accuracy caused by the large difference between the receptive fields of doors and windows, this paper proposes DSPP-YOLO (DenseNet SPP) algorithm. Firstly, on the basis of deepening the network addition, to prevent the loss of shallow location feature information, some residual blocks in YOLOV3 are improved to dense blocks by using the idea of DenseNet. Secondly, the spatial pyramid pooling (SPP) structure is fused into the YOLOV3 feature extraction network to realize multiscale receptive field fusion. Finally, K-means ++ algorithm is used to re-cluster the size of candidate boxes to reduce the error caused by candidate boxes. DSPP-YOLO realizes the position detection of doors and windows by an autonomous robot in an unknown, complex environment. This method is tested. Under the condition of the same data set, the detection accuracy of the DSPP-YOLO algorithm is 77.4% for doors and 38.1% for windows. Compared with YOLOV3 algorithm, the calculation consumption time of the DSPP-YOLO algorithm does not increase, and the detection accuracy of doors is improved by 3.3%, the detection accuracy of windows is improved by 8.8%, and the average accuracy of various types is improved by 6.05%. target detection; DenseNet; YOLOV3; spatial pooling pyramid +
Footnote †: journal: Applied  
as target detection methods based on prior knowledge, such as Selective Search [8] and CPMC [9]. In the subsequent development, people generated candidate regions by the network itself, and gave them a new name anchor box, which formed a new direction for object detection task development. However, for some objects with many common features, such as doors and windows, the target detection results are still not ideal. Therefore, this paper proposes DSPP-YOLO algorithm based on YOLOV3 algorithm improvement. The object detection algorithm based on the convolutional neural network (CNN) can detect the location of doors and windows in unknown complex environments, and meet the requirements of fast detection speed, strong generalization ability and high accuracy of autonomous mobile robots in unknown complex environments. In 2014, [PERSON] et al. proposed R-CNN algorithm [10], which combines CNN with a selective search algorithm and uses Support Vector Machine [11] (SVM) for feature classification and region regression. In 2015, [PERSON] et al. optimized the structure of R-CNN based on SPP-Net [12], and improved the Fast R-CNN algorithm [13]. [PERSON] et al. further generated the Faster R-CNN algorithm based on the Fast R-CNN algorithm, which realized the end-to-end training through Region Proposal Network [14] (RPN). In order to improve the detection performance of small targets, [PERSON] et al. proposed HyperNet [15], which integrates the information of shallow, middle and deep layers. [PERSON] et al. proposed region-based Fully Convolutional Network [16] (RFCN) on the basis of Fully Convolutional Networks (FCN). Despite the high detection accuracy of the two-stage algorithm, it is difficult to improve the detection rate effectively due to the large amount of computation in selecting candidate boxes. The one-stage algorithm realizes feature extraction, classification, and regression in one CNN, treats the whole image as a complete candidate region as input data, and then regresses the position and category information of the target in the graph. In 2016, [PERSON] et al.