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On the 3D backbone network, we have employed adaptive sparse convolution operations to make the backbone network\u2019s channel count more flexible, allowing it to accommodate a wider range of input data types. Furthermore, we have integrated Focal Loss to tackle the issue of class imbalance in detection tasks. Experimental results on the public KITTI dataset demonstrate significant improvements over the PVRCNN++, particularly in pedestrian and bicycle detection tasks. Specifically, we have observed 1% increase in detection accuracy for pedestrians and 2.1% improvement for bicycles. Our detection performance also surpasses that of other comparative detection algorithms.<\/jats:p>","DOI":"10.3233\/jifs-238176","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:52:50Z","timestamp":1710247970000},"page":"11041-11054","source":"Crossref","is-referenced-by-count":1,"title":["SSF: Sparse point cloud object detection based on self-adaptive voxel encoding and focal-sparse convolution"],"prefix":"10.1177","volume":"46","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjian","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Engineering Physics, Shenzhen Technology University, Shenzhen, Guangdong Province"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-238176_ref1","unstructured":"Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds"},{"key":"10.3233\/JIFS-238176_ref2","doi-asserted-by":"crossref","unstructured":"Lv S. , Li X. and Liu B. , Research on 3D Point Cloud Object Detection Methods Based on Deep Learning[C]\/\/2023 2nd International Conference on Big Data, Information and Computer Network (BDICN).IEEE, (2023), 34\u201337.","DOI":"10.1109\/BDICN58493.2023.00014"},{"issue":"12","key":"10.3233\/JIFS-238176_ref3","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep learning for 3d point clouds: A survey[J]","volume":"43","author":"Guo","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"10.3233\/JIFS-238176_ref4","first-page":"7","article-title":"Research overview on 3D object detection methods[J]","volume":"5","author":"Huang","year":"2023","journal-title":"Journal of Intelligent Science and Technology"},{"key":"10.3233\/JIFS-238176_ref5","unstructured":"Guo Yifeng, , Wu Dihao , Wei Qingmin, , A Comprehensive Overview of Deep Learning-Based 3D Object Detection Methods for Point Clouds[J], Application Research of Computers \/ Jisuanji Yingyong Yanjiu 40(1) (2023)."},{"issue":"4","key":"10.3233\/JIFS-238176_ref6","first-page":"72","article-title":"A Review of 3D Object Detection Methods in Unmanned Driving[J]","volume":"39","author":"Ji Yimu,","year":"2019","journal-title":"Journal of Nanjing University of Posts and Telecommunications: Natural Science Edition"},{"key":"10.3233\/JIFS-238176_ref7","doi-asserted-by":"crossref","unstructured":"Zhang Z. , Wang M. , Zhao L. , et al., U-Select RCNN: An Effective Voxel-based 3D Object Detection Method with Feature Selection Strategy[C]\/\/2022 34th Chinese Control and Decision Conference (CCDC). 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