Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar
Long-distance multi-vehicle detection at night is critical in military operations. Due to insufficient light at night, the visual features of vehicles are difficult to distinguish, and many missed detections occur. This paper proposes a two-level detection method for long-distance nighttime multi-ve...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/15/3553 |
| _version_ | 1797440870607224832 |
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| author | Yuanxue Ding Yanchen Qu Jianfeng Sun Dakuan Du Yanze Jiang Hailong Zhang |
| author_facet | Yuanxue Ding Yanchen Qu Jianfeng Sun Dakuan Du Yanze Jiang Hailong Zhang |
| author_sort | Yuanxue Ding |
| collection | DOAJ |
| description | Long-distance multi-vehicle detection at night is critical in military operations. Due to insufficient light at night, the visual features of vehicles are difficult to distinguish, and many missed detections occur. This paper proposes a two-level detection method for long-distance nighttime multi-vehicles based on Gm-APD lidar intensity images and point cloud data. The method is divided into two levels. The first level is 2D detection, which enhances the local contrast of the intensity image and improves the brightness of weak and small objects. With the confidence threshold set, the detection result greater than the threshold is reserved as a reliable object, and the detection result less than the threshold is a suspicious object. In the second level of 3D recognition, the suspicious object area from the first level is converted into the corresponding point cloud classification judgment, and the object detection score is obtained through comprehensive judgment. Finally, the object results of the two-level recognition are merged into the final detection result. Experimental results show that the method achieves a detection accuracy of 96.38% and can effectively improve the detection accuracy of multiple vehicles at night, which is better than the current state-of-the-art detection methods. |
| first_indexed | 2024-03-09T12:14:38Z |
| format | Article |
| id | doaj.art-3f6bdb93275d4004ae85480fd0e0b673 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:14:38Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3f6bdb93275d4004ae85480fd0e0b6732023-11-30T22:47:57ZengMDPI AGRemote Sensing2072-42922022-07-011415355310.3390/rs14153553Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD LidarYuanxue Ding0Yanchen Qu1Jianfeng Sun2Dakuan Du3Yanze Jiang4Hailong Zhang5National Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, ChinaNational Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, ChinaLong-distance multi-vehicle detection at night is critical in military operations. Due to insufficient light at night, the visual features of vehicles are difficult to distinguish, and many missed detections occur. This paper proposes a two-level detection method for long-distance nighttime multi-vehicles based on Gm-APD lidar intensity images and point cloud data. The method is divided into two levels. The first level is 2D detection, which enhances the local contrast of the intensity image and improves the brightness of weak and small objects. With the confidence threshold set, the detection result greater than the threshold is reserved as a reliable object, and the detection result less than the threshold is a suspicious object. In the second level of 3D recognition, the suspicious object area from the first level is converted into the corresponding point cloud classification judgment, and the object detection score is obtained through comprehensive judgment. Finally, the object results of the two-level recognition are merged into the final detection result. Experimental results show that the method achieves a detection accuracy of 96.38% and can effectively improve the detection accuracy of multiple vehicles at night, which is better than the current state-of-the-art detection methods.https://www.mdpi.com/2072-4292/14/15/3553long-distancemulti-vehicle detectionGm-APD lidar |
| spellingShingle | Yuanxue Ding Yanchen Qu Jianfeng Sun Dakuan Du Yanze Jiang Hailong Zhang Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar Remote Sensing long-distance multi-vehicle detection Gm-APD lidar |
| title | Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar |
| title_full | Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar |
| title_fullStr | Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar |
| title_full_unstemmed | Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar |
| title_short | Long-Distance Multi-Vehicle Detection at Night Based on Gm-APD Lidar |
| title_sort | long distance multi vehicle detection at night based on gm apd lidar |
| topic | long-distance multi-vehicle detection Gm-APD lidar |
| url | https://www.mdpi.com/2072-4292/14/15/3553 |
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