Abstract
Object detection in an image aims to point out all the desired objects in the target image and considers the positional enlightenment to obtain machine perspective knowledge. In object detection vanishing gradient has been the most significant problem that can occur, it results in a model with many layers being specified to unable to learn on a specific dataset. It could cause models to a low-grade solution. To get grip on the obstacles, this paper has analyzed certain algorithms for precise object detection from a customized dataset. In this approach, multiple objects in an image have been classified and localized with the help of Mask Region Colvolutional Neural Network (R-CNN). This work is pulled off using a regional convolutional neural network, pixellib, OpenCV, and TensorFlow, resulting in a preferable desired output. Implementing this technique and algorithm, based on deep learning, which is also based on machine learning requires framework understanding. The experimental result and analysis are done with multiple classes of an image to verify the efficiency of the mask R-CNN technique. The algorithm has been compared with other existing object detection strategies to establish the accuracy and reliability of the concerned determined algorithm. The systematic execution enabled clarification of the suggested technique to be precise in analyzing multi-class object in an image.
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References
Saad, A., Mohamed, A.A.: An integrated human computer interaction scheme for object detection using deep learning. Comput. Electr. Eng. 96, 107475 (2021)
Brandenburg, S., Machado, P., Shinde, P., Ferreira, J.F., McGinnity, T.M.: Object classification for robotic platforms. In: Silva, M.F., Luís Lima, J., Reis, L.P., Sanfeliu, A., Tardioli, D. (eds.) ROBOT 2019. AISC, vol. 1093, pp. 199–210. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36150-1_17
Chaudhuri, R., Deb, S.: Machine learning approaches for microscopic image analysis and microbial object detection (MOD) as a decision support system. In: 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR), pp. 1–6. IEEE (2022)
Prajwal, P., Prajwal, D., Harish, D.H., Gajanana, R., Jayasri, B.S., Lokesh, S.: Object detection in self driving cars using deep learning. In: 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp. 1–7 (2021)
Coşkun, M., Uçar, A., Yildirim, Ö., Demir, Y.: Face recognition based on convolutional neural network. In: 2017 International Conference on Modern Electrical and Energy Systems (MEES), pp. 376–379 (2017)
Ji, S., Wei, S., Meng, L.: Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans. Geosci. Remote Sens. 57(1), 574–586 (2019)
Sun, P., et al.: Sparse R-CNN: end-to-end object detection with learnable proposals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14454–14463 (2021)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R., (eds.), Advances in Neural Information Processing Systems, vol. 28. Curran Associates Inc (2015)
Kanimozhi, S., Gayathri, G., Mala, T.: Multiple real-time object identification using single shot multi-box detection. In: 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), pp. 1–5 (2019)
Han, X., Chang, J., Wang, K.: You only look once: unified, real-time object detection. Procedia Comput. Sci. 183, 61–72 (2021)
Lu, X., Kang, X., Nishide, S., Ren, F.: Object detection based on SSD-ResNet. In: 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 89–92 (2019)
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: Detnet: design backbone for object detection. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Nabati, R., Qi, H.: Rrpn: radar region proposal network for object detection in autonomous vehicles. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 3093–3097 (2019)
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Deb, A., Chaudhuri, R., Deb, S. (2023). An Optimal Approach for Multi-class Object Detection. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_24
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DOI: https://doi.org/10.1007/978-3-031-24848-1_24
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