Journal of Infrared and Millimeter Waves, Volume. 44, Issue 1, 111(2025)
Synchronous object detection and matching network based on infrared binocular vision
[1] Z Zou, K Chen, Z Shi et al. Object detection in 20 years: A survey. Proceedings of the IEEE, 111, 257-76(2023).
[2] C Badue, R Guidolini, R V Carneiro et al. Self-driving cars: A survey. Expert systems with applications, 165, 113816(2021).
[3] X Wu, D Ma, X Qu et al. Depth dynamic center difference convolutions for monocular 3d object detection. Neurocomputing, 520, 73-81(2023).
[4] A Loganathan, N S Ahmad. A systematic review on recent advances in autonomous mobile robot navigation. Engineering Science and Technology, an International Journal, 40, 101343(2023).
[5] W Wang, X Wu, X Yuan et al. An experiment-based review of low-light image enhancement methods. Ieee Access, 8, 87884-917(2020).
[6] R Blake, H Wilson. Binocular vision. Vision research, 51, 754-70(2011).
[7] N K Verma, A Goyal, A H Vardhan et al. Object matching using speeded up robust features; proceedings of the Intelligent and Evolutionary Systems: The 19th Asia Pacific Symposium, IES(2015).
[8] S-K Pavani, D Delgado, A F Frangi. Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recognition, 43, 160-72(2010).
[9] Y Li, W Zheng, X Liu et al. Research and improvement of feature detection algorithm based on fast. Rendiconti Lincei Scienze Fisiche e Naturali, 32, 775-89(2021).
[10] P-Y Chen, C-C Huang, C-Y Lien et al. An efficient hardware implementation of hog feature extraction for human detection. IEEE Transactions on Intelligent Transportation Systems, 15, 656-62(2013).
[11] J J Yebes, L M Bergasa, R Arroyo et al. Supervised learning and evaluation of kitti's cars detector with dpm; proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, F, 2014.
[12] H Wang, D Hu. Comparison of svm and ls-svm for regression; proceedings of the 2005 International conference on neural networks and brain, F, 2005.
[13] T Hastie, S Rosset, J Zhu et al. Multi-class adaboost. Statistics and its Interface, 2, 349-60(2009).
[14] P C Ng, S Henikoff. Sift: Predicting amino acid changes that affect protein function. Nucleic acids research, 31, 3812-4(2003).
[15] H Bay, A Ess, T Tuytelaars et al. Speeded-up robust features (surf). Computer vision and image understanding, 110, 346-59(2008).
[16] E Rublee, V Rabaud, K Konolige et al. Orb: An efficient alternative to sift or surf; proceedings of the 2011 International conference on computer vision, F, 2011.
[17] K Han, A Xiao, E Wu et al. Transformer in transformer. Advances in neural information processing systems, 34, 15908-19(2021).
[18] P Bharati, A Pramanik. Deep learning techniques—r-cnn to mask r-cnn: A survey. Computational Intelligence in Pattern Recognition: Proceedings of CIPR, 2020, 657-68(2019).
[19] Z Ge, S Liu, F Wang et al. Yolox: Exceeding yolo series in 2021. arXiv preprint(2021).
[20] W Liu, D Anguelov, D Erhan et al. Ssd: Single shot multibox detector; proceedings of the Computer Vision–ECCV 2016: 14th European Conference(2016).
[21] Z Tian, C Shen, H Chen et al. Fcos: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 1922-33(2020).
[22] M Krišto, M Ivasic-Kos, M Pobar. Thermal object detection in difficult weather conditions using yolo. IEEE access, 8, 125459-76(2020).
[23] S Ren, K He, R Girshick et al. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28(2015).
[24] Z Cai, N Vasconcelos. Cascade r-cnn: Delving into high quality object detection; proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, F, 2018.
[25] J Redmon, A Farhadi. Yolov3: An incremental improvement. arXiv preprint(2018).
[26] S Yao, Q Zhu, T Zhang et al. Infrared image small-target detection based on improved fcos and spatio-temporal features. Electronics, 11, 933(2022).
[27] F Lin, K Bao, Y Li et al. Learning contrast-enhanced shape-biased representations for infrared small target detection. IEEE Transactions on Image Processing(2024).
[28] F Lin, S Ge, K Bao et al. Learning shape-biased representations for infrared small target detection. IEEE Transactions on Multimedia(2023).
[29] P-E Sarlin, D DeTone, T Malisiewicz et al. Superglue: Learning feature matching with graph neural networks; proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, F, 2020.
[30] J Sun, Z Shen, Y Wang et al. Loftr: Detector-free local feature matching with transformers; proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, F, 2021.
[31] J Li, P Wang, P Xiong et al. Practical stereo matching via cascaded recurrent network with adaptive correlation; proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, F, 2022.
[32] C Shorten, T M Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of big data, 6, 1-48(2019).
[33] S Patro, K K Sahu. Normalization: A preprocessing stage. arXiv preprint(2015).
[34] B Koonce, B Koonce. Resnet 50. Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization, 63-72(2021).
[35] M Sandler, A Howard, M Zhu et al. Mobilenetv2: Inverted residuals and linear bottlenecks; proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, F, 2018.
[36] N Ma, X Zhang, H-T Zheng et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design; proceedings of the Proceedings of the European conference on computer vision (ECCV), F, 2018.
[37] M Tan, Q Le. Efficientnetv2: Smaller models and faster training; proceedings of the International conference on machine learning, F, 2021.
[38] T-Y Lin, P Dollár, R Girshick et al. Feature pyramid networks for object detection; proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, F, 2017.
[39] T-Y Lin, P Goyal, R Girshick et al. Focal loss for dense object detection; proceedings of the Proceedings of the IEEE international conference on computer vision, F, 2017.
[40] Z Zheng, P Wang, W Liu et al. Distance-iou loss: Faster and better learning for bounding box regression; proceedings of the Proceedings of the AAAI conference on artificial intelligence, F, 2020.
[41] J Su, Z Liu, J Zhang et al. Dv-net: Accurate liver vessel segmentation via dense connection model with d-bce loss function. Knowledge-Based Systems, 232, 107471(2021).
[42] D Chicco. Siamese neural networks: An overview. Artificial neural networks, 73-94(2021).
Get Citation
Copy Citation Text
Chang-Wen ZENG, Zhi-Yu YANG, Zuo-Xiao DAI, Ming-Jian GU. Synchronous object detection and matching network based on infrared binocular vision[J]. Journal of Infrared and Millimeter Waves, 2025, 44(1): 111
Category: Interdisciplinary Research on Infrared Science
Received: Apr. 17, 2024
Accepted: --
Published Online: Mar. 5, 2025
The Author Email: Ming-Jian GU (gumingj@sina.com)