Target Detection and Classification Based on LiDAR
Keywords:, LiDAR, Target detection and classification, RDforest new model, Seed area, Classification credibility.
To solve the problem of difficult classification of air baggage, we use LMS511 LiDAR to collect the distance data from the baggage surface to the light-center of LiDAR, propose a new detection and classification algorithm, and the baggage detection and classification system is designed, thus, the self-service of air baggage check is realized. Firstly, an object-based classification method is proposed by considering the characteristics of target. The geometry, texture, corner features and shape descriptors of the baggage are extracted to construct the feature vectors, and the feature vectors are imported into the RDforest new model to classify the baggage samples. Secondly, based on the three-dimensional characteristics of LiDAR data, a classification method based on Seed area is proposed. By comparing the classification credibility values of two classification methods, the further classification results reached 91.33%. In addition, the filling rate and the average Gaussian curvature entropy were used to classify the hard shell packing box and the Luggage case in detail, and the classification results reached 100%. The experimental results show that target detection and classification system is more robust and has better recognition and classification effects.
 GAO Q, LI T, LUO Q. An Algorithm for Inspecting the Number of Self Check-In Airline Luggage Based on Hierarchical Clustering[C]// Sixth International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2014:71-74.
 LEE M, HUR S, PARK Y. An Obstacle Classification Method Using Multi-feature Comparison Based on 2D LIDAR Database[C]// International Conference on Information Technology - New Generations. IEEE, 2015:674-679.
 LI P, WEI Z H, HE X, et al. Object recognition based on shape feature fusion under multi-views[J]. Optics & Precision Engineering, 2014, 22(12):3368-3376.
 ZHANG Y H, GENG G H, WEI X R, SHI C C, ZHANG S L. Feature extraction of point clouds using the DBSCAN clustering [J]. JOURNAL OF XIDIAN UNIVERSITY, 2017, 44(2): 114-120.
 SUN J, LAI Z. Airborne LiDAR Feature Selection for Urban Classification Using Random Forests[J]. Geomatics & Information Science of Wuhan University, 2014, 39(11): 1310-1313.
 SHANG M S, WANG K C. Advanced Image Registration Method Based on Harris and SIFT Algorith [J]. MICROELECTRONICS & COMPUTER, 2018, 35(6) :132-134.
 SREEVALSAN-NAIR J, JINDAL A. Using gradients and tensor voting in 3D local geometric descriptors for feature detection in airborne lidar point clouds in urban regions[C]// IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2017:5881-5884.
 ZHOU Z H, FENG J. Deep forest: Towards an alternative to deepneural networks[J]. arXiv preprint arXiv:1702.08835, 2017.
 LY A, MARSMAN M, WAGENMAKERS E J. Analytic posteriors for Pearson's correlation coefficient[J]. Statistica Neerlandica, 2018, 72(1):4.
 ZHOU B N, MIN H S, KANG Y W. Research on 3D Point Cloud Segmentation Algorithm in PCL Environment [J]. MICROELECTRONICS & COMPUTER, 2018, 35(6):101-105.
 KIM D, JANG H U, MUN S M, et al. Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks[J]. IEEE Signal Processing Letters, 2018, PP(99):1-1.
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