Abstract
Deep learning is gaining popularity in the realm of object localization. Existing deep learning methods have shown good accuracy and inference runtime, but they require a lot of training data. This needs a major investment in resources, especially for offshore industrial sites that lack huge datasets. Furthermore, because the inference set should contain the same types of objects as the training set, deep learning solutions are highly sensitive to object types. To address these two challenging issues, we proposed a novel framework based on image retrieval and matching algorithms. The set of relevant images to the object query is first retrieved using the Bag of Words. Furthermore, we developed two alternative image matching algorithms to localize the object query on the relevant images. The first one is based on generate and test, and the second one is based on geometric verification. Extensive simulation has been carried out to validate the suggest methodology, and the results are highly promising in terms of computing time and accuracy.
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References
Wang, W., Lai, Q., Fu, H., Shen, J., Ling, H., Yang, R.: Salient object detection in the deep learning era: an in-depth survey. IEEE Trans. Pattern Anal. Mach. Intell. (2021)
Aftf, M., Ayachi, R., Said, Y., Pissaloux, E., Atri, M.: Indoor object c1assification for autonomous navigation assistance based on deep cnn model. In: 2019 IEEE International Symposium on Measurements & Networking (M &N), pp. 1–4. IEEE (2019)
Liu, Y., Sun, P., Wergeles, N., Shang, Y.: A survey and performance evaluation of deep learning methods for small object detection. Expert Syst. Appl. 172, 114602 (2021)
Boukerche, A., Hou, Z.: Object detection using deep learning methods in traffic scenarios. ACM Comput. Surv. (CSUR) 54(2), 1–35 (2021)
Kim, J.J.Y., Urschler, M., Riddle, P.J., Wicker, J.: Symbiolcd: Ensemble-based loop closure detection using CNN-extracted objects and visual bag-of-words. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5425–5425. IEEE (2021)
Garg, M., Dhiman, G.: A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Comput. Appl. 33, 1311–1328 (2021). https://doi.org/10.1007/s00521-020-05017-z
Zhang, Z., Zhu, X., Guangming, L., Zhang, Y.: Probability ordinal-preserving semantic hashing for large-scale image retrieval. ACM Trans. Knowl. Discov. Data (TKDD) 15(3), 1–22 (2021)
Khade, R., Jariwala, K., Chattopadhyay, C., Pal, U.: A rotation and scale invariant approach for multi-oriented floor plan image retrieval. Pattern Recogn. Lett. 145, 1–7 (2021)
Jia, S., Ma, L., Yang, S., Qin, D.: Semantic and context based image retrieval method using a single image sensor for visual indoor positioning. IEEE Sens. J. (2021)
Yin, X., Ma, L., Tan, X.: A novel image retrieval method for image based localization in large-scale environment. In: 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 1–5. IEEE (2021)
Djenouri, Y., Hjelmervik, J.: Hybrid decomposition convolution neural network and vocabulary forest for image retrieval. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3064–3070. IEEE (2021)
Salazar, J.D., et al.: 3d photogrammetric inspection of risers using RPAS and deep learning in oil and gas offshore platforms. Int. Arch. Photogrammetry Remote Sens. Spatial Inf. Sci. 43, 1265–1272 (2020)
Gong, F., Ma, Y., Zheng, P., Song, T.: A deep model method for recognizing activities of workers on offshore drilling platform by multistage convolutional pose machine. J. Loss Prev. Process Ind. 64, 104043 (2020)
Hossein-Nejad, Z., Agahi, H., Mahmoodzadeh, A.: Image matching based on the adaptive redundant keypoint elimination method in the sift algorithm. Pattern Anal. Appl. 24(2), 669–683 (2021). https://doi.org/10.1007/s10044-020-00938-w
Wang, Y., Zhao, R., Liang, L., Zheng, X., Cen, Y., Kan, S.: Block-based image matching for image retrieval. J. Vis. Commun. Image Represent. 74, 102998 (2021)
Wu, J., Zhang, L., Liu, Y., Chen, K.: Real-time vanishing point detector integrating under-parameterized ransac and hough transform. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3732–3741 (2021)
Djenouri, Y., Hatleskog, J., Hjelmervik, J., Bjorne, E., Utstumo, T., Mobarhan, M.: Deep learning based decomposition for visual navigation in industrial platforms. Appl. Intell. 52(7), 8101–8117 (2022). https://doi.org/10.1007/s10489-021-02908-z
Yang, X., Gao, X., Song, B., Han, B.: Hierarchical deep embedding for aurora image retrieval. IEEE Trans. Cybern. (2020)
Giveki, D.: Scale-space multi-view bag of words for scene categorization. Multimedia Tools Appl. 80(1), 1223–1245 (2021). https://doi.org/10.1007/s11042-020-09759-9
Djenouri, Y., Belhadi, A., Fournier-Viger, P., Lin, J.C.W.: Fast and effective cluster-based information retrieval using frequent closed itemsets. Inf. Sci. 453, 154–167 (2018)
Acknowledgements
This paper is supported by the Norwegian Research Council funded project Advanced 3D visualization and AR for industrial operations. We would like to thank all project partners, including Aker BP, Lundin, Aker Solutions and Kværner for sharing ideas and data.
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Djenouri, Y., Hjelmervik, J., Bjorne, E., Mobarhan, M. (2022). How Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_26
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