A Machine Learning Framework for Real-Time Object Detection and Recognition in Complex Environments
Keywords:
Object detection, machine learning, real-time recognition, deep learning, computer vision, complex environmentsAbstract
This study explores a machine learning framework for real-time object detection and recognition in complex environments through a systematic analysis of contemporary deep learning models. The research synthesises secondary data from recent scholarly works to evaluate the performance of major detection architectures, including convolutional neural network-based, hybrid, and transformer-based approaches. The findings indicate that while significant progress has been made in improving detection accuracy and processing speed, challenges related to occlusion, scale variation, and computational constraints continue to affect performance in real-world scenarios. The study highlights the importance of multi-scale feature extraction, attention mechanisms, and lightweight model design in enhancing detection robustness and efficiency. It also emphasises the growing relevance of deploying optimised frameworks on edge devices for real-time applications across domains such as autonomous systems, surveillance, and healthcare.
References
Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European Conference on Computer Vision (ECCV) (pp. 213–229). Springer.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR).
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research, 32(11), 1231–1237.
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 2961–2969).
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2117–2125).
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European Conference on Computer Vision (ECCV) (pp. 21–37). Springer.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779–788).
Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 91–99).
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (ICML) (pp. 6105–6114).
Tan, M., Pang, R., & Le, Q. V. (2020). EfficientDet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10781–10790).
Zhang, Q., Yang, L., & Li, X. (2022). Hybrid attention-based object detection in complex environments. IEEE Access, 10, 45678–45689.
Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232.
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