Position Detection of Doors and Windows Based on DSPP-YOLO
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 simil...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10770 |
| _version_ | 1797469291577081856 |
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| author | Tong Zhang Jiaqi Li Yilei Jiang Mengqi Zeng Minghui Pang |
| author_facet | Tong Zhang Jiaqi Li Yilei Jiang Mengqi Zeng Minghui Pang |
| author_sort | Tong Zhang |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-09T19:19:18Z |
| format | Article |
| id | doaj.art-a5079d205cd34607adb9b4060eb78903 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:19:18Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a5079d205cd34607adb9b4060eb789032023-11-24T03:32:36ZengMDPI AGApplied Sciences2076-34172022-10-0112211077010.3390/app122110770Position Detection of Doors and Windows Based on DSPP-YOLOTong Zhang0Jiaqi Li1Yilei Jiang2Mengqi Zeng3Minghui Pang4Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaAutonomous 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%.https://www.mdpi.com/2076-3417/12/21/10770target detectionDenseNetYOLOV3spatial pooling pyramid |
| spellingShingle | Tong Zhang Jiaqi Li Yilei Jiang Mengqi Zeng Minghui Pang Position Detection of Doors and Windows Based on DSPP-YOLO Applied Sciences target detection DenseNet YOLOV3 spatial pooling pyramid |
| title | Position Detection of Doors and Windows Based on DSPP-YOLO |
| title_full | Position Detection of Doors and Windows Based on DSPP-YOLO |
| title_fullStr | Position Detection of Doors and Windows Based on DSPP-YOLO |
| title_full_unstemmed | Position Detection of Doors and Windows Based on DSPP-YOLO |
| title_short | Position Detection of Doors and Windows Based on DSPP-YOLO |
| title_sort | position detection of doors and windows based on dspp yolo |
| topic | target detection DenseNet YOLOV3 spatial pooling pyramid |
| url | https://www.mdpi.com/2076-3417/12/21/10770 |
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