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Syst."],"published-print":{"date-parts":[[2022,12,31]]},"abstract":"<jats:p>For Internet of Vehicles applications, reliable autonomous driving systems usually perform the majority of their computations on the cloud due to the limited computing power of edge devices. The communication delay between cloud platforms and edge devices, however, can cause dangerous consequences, particularly for latency-sensitive object detection tasks. Object detection tasks are also vulnerable to significantly degraded model performance caused by unknown objects, which creates unsafe driving conditions. To address these problems, this study develops an orchestrated system that allows real-time object detection and incrementally learns unknown objects in a complex and dynamic environment. A you-only-look-once\u2013based object detection model in edge computing mode uses thermal images to detect objects accurately in poor lighting conditions. In addition, an attention mechanism improves the system\u2019s performance without significantly increasing model complexity. An unknown object detector automatically classifies and labels unknown objects without direct supervision on edge devices, while a roadside unit (RSU)-based mechanism is developed to update classes and ensure a secure driving experience for autonomous vehicles. Moreover, the interactions between edge devices, RSU servers, and the cloud are designed to allow efficient collaboration. The experimental results indicate that the proposed system learns uncategorized objects dynamically and detects instances accurately.<\/jats:p>","DOI":"10.1145\/3554923","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T12:35:55Z","timestamp":1667565355000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Roadside Unit-based Unknown Object Detection in Adverse Weather Conditions for Smart Internet of Vehicles"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3990-7932","authenticated-orcid":false,"given":"Yu-Chia","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Economics, National Taiwan University, Taipei, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4481-1633","authenticated-orcid":false,"given":"Sin-Ye","family":"Jhong","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2665-0821","authenticated-orcid":false,"given":"Chih-Hsien","family":"Hsia","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Ilan University, Yilan County, Taiwan"}]}],"member":"320","published-online":{"date-parts":[[2023,1,3]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"6473","article-title":"Deployment of IoV for smart cities: Applications, architecture, and challenges","volume":"7","author":"Ang Li-Minn","year":"2018","unstructured":"Li-Minn Ang, Kah Phooi Seng, Gerald K. 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Syst."},{"key":"e_1_3_1_7_2","first-page":"1","volume-title":"Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS\u201919)","author":"Chen Yung-Yao","year":"2019","unstructured":"Yung-Yao Chen, Sin-Ye Jhong, Guan-Yi Li, and Ping-Han Chen. 2019. Thermal-based pedestrian detection using faster r-cnn and region decomposition branch. In Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS\u201919). IEEE, 1\u20132."},{"issue":"5","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.1109\/JIOT.2017.2690902","article-title":"Internet of vehicles: Architecture, protocols, and security","volume":"5","author":"Contreras-Castillo Juan","year":"2017","unstructured":"Juan Contreras-Castillo, Sherali Zeadally, and Juan Antonio Guerrero-Iba\u00f1ez. 2017. Internet of vehicles: Architecture, protocols, and security. 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In Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning. 7\u201312."},{"key":"e_1_3_1_11_2","first-page":"1","volume-title":"Proceedings of the 21st International Arab Conference on Information Technology (ACIT\u201920)","author":"Fadhil Jawaher Abdulwahab","year":"2020","unstructured":"Jawaher Abdulwahab Fadhil and Qusay Idrees Sarhan. 2020. Internet of vehicles (IoV): A survey of challenges and solutions. In Proceedings of the 21st International Arab Conference on Information Technology (ACIT\u201920). IEEE, 1\u201310."},{"key":"e_1_3_1_12_2","first-page":"9","volume-title":"Proceedings of the IEEE International Conference on Edge Computing (EDGE\u201920)","author":"Fu Yanjin","year":"2020","unstructured":"Yanjin Fu, Daxin Tian, Xunting Duan, Jianshan Zhou, Ping Lang, Chunmian Lin, and Xin You. 2020. A camera\u2013radar fusion method based on edge computing. In Proceedings of the IEEE International Conference on Edge Computing (EDGE\u201920). 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IEEE, 341\u2013343."},{"key":"e_1_3_1_17_2","first-page":"13713","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hou Qibin","year":"2021","unstructured":"Qibin Hou, Daquan Zhou, and Jiashi Feng. 2021. Coordinate attention for efficient mobile network design. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13713\u201313722."},{"key":"e_1_3_1_18_2","first-page":"7132","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Hu Jie","year":"2018","unstructured":"Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7132\u20137141."},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/CloudCom.2012.6427481","volume-title":"Proceedings of the 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings","author":"Hussain Rasheed","year":"2012","unstructured":"Rasheed Hussain, Junggab Son, Hasoo Eun, Sangjin Kim, and Heekuck Oh. 2012. Rethinking vehicular communications: Merging VANET with cloud computing. In Proceedings of the 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings. IEEE, 606\u2013609."},{"key":"e_1_3_1_20_2","first-page":"1037","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Hwang Soonmin","year":"2015","unstructured":"Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, and In So Kweon. 2015. Multispectral pedestrian detection: Benchmark dataset and baseline. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1037\u20131045."},{"issue":"4","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s11554-021-01110-1","article-title":"Nighttime object detection system with lightweight deep network for internet of vehicles","volume":"18","author":"Jhong Sin-Ye","year":"2021","unstructured":"Sin-Ye Jhong, Yung-Yao Chen, Chih-Hsien Hsia, Shih-Chang Lin, Kuo-Hua Hsu, and Chin-Feng Lai. 2021. Nighttime object detection system with lightweight deep network for internet of vehicles. J. Real-Time Image Process. 18, 4 (2021), 1141\u20131155.","journal-title":"J. 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Bioinform."},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"125459","DOI":"10.1109\/ACCESS.2020.3007481","article-title":"Thermal object detection in difficult weather conditions using YOLO","volume":"8","author":"Kri\u0161to Mate","year":"2020","unstructured":"Mate Kri\u0161to, Marina Ivasic-Kos, and Miran Pobar. 2020. Thermal object detection in difficult weather conditions using YOLO. IEEE Access 8 (2020), 125459\u2013125476.","journal-title":"IEEE Access"},{"issue":"2","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/MCOM.2014.6736756","article-title":"Vehicular cloud networking: Architecture and design principles","volume":"52","author":"Lee Euisin","year":"2014","unstructured":"Euisin Lee, Eun-Kyu Lee, Mario Gerla, and Soon Y. Oh. 2014. Vehicular cloud networking: Architecture and design principles. IEEE Commun. Mag. 52, 2 (2014), 148\u2013155.","journal-title":"IEEE Commun. Mag."},{"key":"e_1_3_1_29_2","first-page":"3190","volume-title":"Proceedings of the International Conference on Electronic and Mechanical Engineering and Information Technology","volume":"6","author":"Leng Ying","year":"2011","unstructured":"Ying Leng and Lingshu Zhao. 2011. Novel design of intelligent internet-of-vehicles management system based on cloud-computing and internet-of-things. In Proceedings of the International Conference on Electronic and Mechanical Engineering and Information Technology, Vol. 6. IEEE, 3190\u20133193."},{"issue":"1","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2676869","article-title":"A network behavior-based botnet detection mechanism using PSO and K-means","volume":"6","author":"Li Shing-Han","year":"2015","unstructured":"Shing-Han Li, Yu-Cheng Kao, Zong-Cyuan Zhang, Ying-Ping Chuang, and David C. Yen. 2015. A network behavior-based botnet detection mechanism using PSO and K-means. ACM Trans. Manage. Info. Syst. 6, 1 (2015), 1\u201330.","journal-title":"ACM Trans. Manage. Info. Syst."},{"key":"e_1_3_1_31_2","first-page":"2980","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Lin Tsung-Yi","year":"2017","unstructured":"Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Doll\u00e1r. 2017. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision. 2980\u20132988."},{"key":"e_1_3_1_32_2","first-page":"740","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Lin Tsung-Yi","year":"2014","unstructured":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Proceedings of the European Conference on Computer Vision. Springer, 740\u2013755."},{"issue":"8","key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1109\/JPROC.2019.2915983","article-title":"Edge computing for autonomous driving: Opportunities and challenges","volume":"107","author":"Liu Shaoshan","year":"2019","unstructured":"Shaoshan Liu, Liangkai Liu, Jie Tang, Bo Yu, Yifan Wang, and Weisong Shi. 2019. Edge computing for autonomous driving: Opportunities and challenges. Proc. IEEE 107, 8 (2019), 1697\u20131716.","journal-title":"Proc. IEEE"},{"key":"e_1_3_1_34_2","first-page":"21","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Liu Wei","year":"2016","unstructured":"Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. 2016. Ssd: Single shot multibox detector. In Proceedings of the European Conference on Computer Vision. Springer, 21\u201337."},{"key":"e_1_3_1_35_2","first-page":"211","volume-title":"Proceedings of the IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI\u201917)","author":"Lopez H\u00e9ctor Jalil Desirena","year":"2017","unstructured":"H\u00e9ctor Jalil Desirena Lopez, Mario Siller, and Iv\u00e1n Huerta. 2017. Internet of vehicles: Cloud and fog computing approaches. In Proceedings of the IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI\u201917). IEEE, 211\u2013216."},{"issue":"4","key":"e_1_3_1_36_2","first-page":"2048","article-title":"Diversified technologies in internet of vehicles under intelligent edge computing","volume":"22","author":"Lv Zhihan","year":"2020","unstructured":"Zhihan Lv, Dongliang Chen, and Qingjun Wang. 2020. Diversified technologies in internet of vehicles under intelligent edge computing. IEEE Trans. Intell. Transport. Syst. 22, 4 (2020), 2048\u20132059.","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"issue":"1","key":"e_1_3_1_37_2","first-page":"1","article-title":"Novel machine learning for big data analytics in intelligent support information management systems","volume":"13","author":"Lv Zhihan","year":"2021","unstructured":"Zhihan Lv, Ranran Lou, Hailin Feng, Dongliang Chen, and Haibin Lv. 2021. Novel machine learning for big data analytics in intelligent support information management systems. ACM Trans. Manage. Info. Syst. 13, 1 (2021), 1\u201321.","journal-title":"ACM Trans. Manage. Info. Syst."},{"key":"e_1_3_1_38_2","first-page":"3570","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Miller Dimity","year":"2021","unstructured":"Dimity Miller, Niko Sunderhauf, Michael Milford, and Feras Dayoub. 2021. Class anchor clustering: A loss for distance-based open set recognition. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 3570\u20133578."},{"issue":"4","key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3382159","article-title":"Edge-based intrusion detection for IoT devices","volume":"11","author":"Mudgerikar Anand","year":"2020","unstructured":"Anand Mudgerikar, Puneet Sharma, and Elisa Bertino. 2020. Edge-based intrusion detection for IoT devices. ACM Trans. Manage. Info. Syst. 11, 4 (2020), 1\u201321.","journal-title":"ACM Trans. Manage. Info. Syst."},{"key":"e_1_3_1_40_2","first-page":"807","volume-title":"Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML\u201910)","author":"Nair Vinod","year":"2010","unstructured":"Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML\u201910). Omnipress, Madison, WI, 807\u2013814."},{"issue":"9","key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"3756","DOI":"10.1109\/TITS.2019.2932802","article-title":"Novel convolutional neural network-based roadside unit for accurate pedestrian localisation","volume":"21","author":"Ojala Risto","year":"2019","unstructured":"Risto Ojala, Jari Veps\u00e4l\u00e4inen, Jussi Hanhirova, Vesa Hirvisalo, and Kari Tammi. 2019. Novel convolutional neural network-based roadside unit for accurate pedestrian localisation. IEEE Trans. Intell. Transport. Syst. 21, 9 (2019), 3756\u20133765.","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"e_1_3_1_42_2","first-page":"11814","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Perera Pramuditha","year":"2020","unstructured":"Pramuditha Perera, Vlad I. Morariu, Rajiv Jain, Varun Manjunatha, Curtis Wigington, Vicente Ordonez, and Vishal M. Patel. 2020. Generative-discriminative feature representations for open-set recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 11814\u201311823."},{"key":"e_1_3_1_43_2","article-title":"Generative probabilistic novelty detection with adversarial autoencoders","volume":"31","author":"Pidhorskyi Stanislav","year":"2018","unstructured":"Stanislav Pidhorskyi, Ranya Almohsen, and Gianfranco Doretto. 2018. Generative probabilistic novelty detection with adversarial autoencoders. Adv. Neural Info. Process. Syst. 31 (2018).","journal-title":"Adv. Neural Info. Process. Syst."},{"key":"e_1_3_1_44_2","first-page":"779","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Redmon Joseph","year":"2016","unstructured":"Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779\u2013788."},{"key":"e_1_3_1_45_2","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren Shaoqing","year":"2015","unstructured":"Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Info. Process. Syst. 28 (2015).","journal-title":"Adv. Neural Info. Process. Syst."},{"issue":"1","key":"e_1_3_1_46_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.23956\/ijarcsse.v8i1.512","article-title":"Internet of vehicles: An introduction","volume":"8","author":"Sadiku Matthew N. O.","year":"2018","unstructured":"Matthew N. O. Sadiku, Mahamadou Tembely, and Sarhan M. Musa. 2018. Internet of vehicles: An introduction. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 8, 1 (2018), 11.","journal-title":"Int. J. Adv. Res. Comput. Sci. Softw. 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IEEE, 413\u2013414."},{"key":"e_1_3_1_49_2","first-page":"1283","volume-title":"Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES\u201920)","author":"Talluri Pavan","year":"2020","unstructured":"Pavan Talluri and Mohit Dua. 2020. Low-resolution human identification in thermal imagery. In Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES\u201920). IEEE, 1283\u20131287."},{"key":"e_1_3_1_50_2","first-page":"10781","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Tan Mingxing","year":"2020","unstructured":"Mingxing Tan, Ruoming Pang, and Quoc V. Le. 2020. Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 10781\u201310790."},{"issue":"1","key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3365445","article-title":"Pulmonary nodule detection based on isodata-improved faster RCNN and 3D-CNN with focal loss","volume":"16","author":"Tong Chao","year":"2020","unstructured":"Chao Tong, Baoyu Liang, Mengze Zhang, Rongshan Chen, Arun Kumar Sangaiah, Zhigao Zheng, Tao Wan, Chenyang Yue, and Xinyi Yang. 2020. Pulmonary nodule detection based on isodata-improved faster RCNN and 3D-CNN with focal loss. ACM Trans. Multimedia Comput., Commun. Appl. 16, 1s (2020), 1\u20139.","journal-title":"ACM Trans. Multimedia Comput., Commun. Appl."},{"key":"e_1_3_1_52_2","first-page":"1","volume-title":"Proceedings of the 8th International Conference on Image Processing Theory, Tools and Applications (IPTA\u201918)","author":"Ullah Asad","year":"2018","unstructured":"Asad Ullah, Hongmei Xie, Muhammad Omer Farooq, and Zhaoyun Sun. 2018. Pedestrian detection in infrared images using fast RCNN. In Proceedings of the 8th International Conference on Image Processing Theory, Tools and Applications (IPTA\u201918). IEEE, 1\u20136."},{"key":"e_1_3_1_53_2","first-page":"13029","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Chien-Yao","year":"2021","unstructured":"Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. 2021. Scaled-YOLOv4: Scaling cross stage partial network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13029\u201313038."},{"key":"e_1_3_1_54_2","first-page":"1","volume-title":"Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP\u201911)","author":"Wang Jin","year":"2011","unstructured":"Jin Wang, Jinsong Cho, Sungyoung Lee, and Tinghuai Ma. 2011. Real time services for future cloud computing enabled vehicle networks. In Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP\u201911). IEEE, 1\u20135."},{"issue":"2","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/COMST.2018.2888904","article-title":"Networking and communications in autonomous driving: A survey","volume":"21","author":"Wang Jiadai","year":"2018","unstructured":"Jiadai Wang, Jiajia Liu, and Nei Kato. 2018. Networking and communications in autonomous driving: A survey. IEEE Commun. Surveys Tutor. 21, 2 (2018), 1243\u20131274.","journal-title":"IEEE Commun. Surveys Tutor."},{"issue":"1","key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3466780","article-title":"LogoDet-3K: A large-scale image dataset for logo detection","volume":"18","author":"Wang Jing","year":"2022","unstructured":"Jing Wang, Weiqing Min, Sujuan Hou, Shengnan Ma, Yuanjie Zheng, and Shuqiang Jiang. 2022. LogoDet-3K: A large-scale image dataset for logo detection. ACM Trans. Multimedia Comput., Commun. Appl. 18, 1 (2022), 1\u201319.","journal-title":"ACM Trans. Multimedia Comput., Commun. Appl."},{"key":"e_1_3_1_57_2","first-page":"36","volume-title":"Proceedings of the International Conference on Smart Grid and Electrical Automation (ICSGEA\u201919)","author":"Xu Kan","year":"2019","unstructured":"Kan Xu, Zhiwen Yuan, Jinli Zhang, Yiping Ji, Xing He, and Haosen Yang. 2019. SF6 gas infrared thermal imaging leakage detection based on faster-RCNN. In Proceedings of the International Conference on Smart Grid and Electrical Automation (ICSGEA\u201919). IEEE, 36\u201340."},{"key":"e_1_3_1_58_2","first-page":"178","volume-title":"Proceedings of the IEEE International Conference on Electro Information Technology (EIT\u201921)","author":"Xu Xiangyu","year":"2021","unstructured":"Xiangyu Xu, Xiang Cao, Gregory Wolffe, and Jie Du. 2021. Intelligent roadside unit deployment in vehicular network. In Proceedings of the IEEE International Conference on Electro Information Technology (EIT\u201921). IEEE, 178\u2013183."},{"issue":"2","key":"e_1_3_1_59_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3401979","article-title":"Tripres: Traffic flow prediction driven resource reservation for multimedia IoV with edge computing","volume":"17","author":"Xu Xiaolong","year":"2021","unstructured":"Xiaolong Xu, Zijie Fang, Lianyong Qi, Xuyun Zhang, Qiang He, and Xiaokang Zhou. 2021. 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Wireless Netw. 26, 3 (2020), 1611\u20131629.","journal-title":"Wireless Netw."},{"issue":"1","key":"e_1_3_1_61_2","first-page":"1","article-title":"Exploring image enhancement for salient object detection in low light images","volume":"17","author":"Xu Xin","year":"2021","unstructured":"Xin Xu, Shiqin Wang, Zheng Wang, Xiaolong Zhang, and Ruimin Hu. 2021. Exploring image enhancement for salient object detection in low light images. ACM Trans. Multimedia Comput., Commun. Appl. 17, 1s (2021), 1\u201319.","journal-title":"ACM Trans. Multimedia Comput., Commun. Appl."},{"key":"e_1_3_1_62_2","article-title":"CNN-based color and thermal image fusion for object detection in automated driving","author":"Yadav Ravi","year":"2020","unstructured":"Ravi Yadav, Ahmed Samir, Hazem Rashed, Senthil Yogamani, and Rozenn Dahyot. 2020. CNN-based color and thermal image fusion for object detection in automated driving. Irish Mach. Vision Image Process Conf. (2020), 69--76.","journal-title":"Irish Mach. Vision Image Process Conf."},{"issue":"10","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/CC.2014.6969789","article-title":"An overview of internet of vehicles","volume":"11","author":"Yang Fangchun","year":"2014","unstructured":"Fangchun Yang, Shangguang Wang, Jinglin Li, Zhihan Liu, and Qibo Sun. 2014. An overview of internet of vehicles. China Commun. 11, 10 (2014), 1\u201315.","journal-title":"China Commun."},{"issue":"2","key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"202","DOI":"10.3103\/S0146411621020097","article-title":"Detecting small objects in thermal images using single-shot detector","volume":"55","author":"Zhang Hao","year":"2021","unstructured":"Hao Zhang, Xiang-gong Hong, and Li Zhu. 2021. Detecting small objects in thermal images using single-shot detector. Autom. Control Comput. Sci. 55, 2 (2021), 202\u2013211.","journal-title":"Autom. Control Comput. Sci."},{"key":"e_1_3_1_65_2","article-title":"Enhancing geometric factors in model learning and inference for object detection and instance segmentation","author":"Zheng Zhaohui","year":"2021","unstructured":"Zhaohui Zheng, Ping Wang, Dongwei Ren, Wei Liu, Rongguang Ye, Qinghua Hu, and Wangmeng Zuo. 2021. Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybernet. 52, 8 (2021), 8574--8586.","journal-title":"IEEE Trans. Cybernet."},{"issue":"10","key":"e_1_3_1_66_2","doi-asserted-by":"crossref","first-page":"12187","DOI":"10.1109\/TVT.2020.3015127","article-title":"Edge-facilitated augmented vision in vehicle-to-everything networks","volume":"69","author":"Zhou Pengyuan","year":"2020","unstructured":"Pengyuan Zhou, Tristan Braud, Aleksandr Zavodovski, Zhi Liu, Xianfu Chen, Pan Hui, and Jussi Kangasharju. 2020. Edge-facilitated augmented vision in vehicle-to-everything networks. IEEE Trans. Vehic. Technol. 69, 10 (2020), 12187\u201312201.","journal-title":"IEEE Trans. Vehic. Technol."},{"issue":"4","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3466690","article-title":"AIDCOV: An interpretable artificial intelligence model for detection of COVID-19 from chest radiography images","volume":"12","author":"Zokaeinikoo Maryam","year":"2021","unstructured":"Maryam Zokaeinikoo, Pooyan Kazemian, Prasenjit Mitra, and Soundar Kumara. 2021. AIDCOV: An interpretable artificial intelligence model for detection of COVID-19 from chest radiography images. ACM Trans. Manage. Info. Syst. 12, 4 (2021), 1\u201320.","journal-title":"ACM Trans. Manage. Info. 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