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An In-Depth Exploration of Object Detection: Techniques, Applications, and Advancements
Authors
1 Department of CSE, Garden City University, Bengaluru, Karnataka, India. 2 Department of Computer Science and Engineering, GIET University, Gunupur, India. 3 Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India. 4 Department of Engineering, Garden City University, Bengaluru, India. 5 Department of Electronics and communication engineering, KL University, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 178-185
- Article Type
- Research Article
- Check for updates
-
References
1. Ali, Mahmoud Atta Mohammed, et al. "Advancing Crowd Object Detection: A Review of YOLO, CNN and ViTs Hybrid Approach." Journal of Intelligent Learning Systems and Applications 16.3 (2024): 175-221.
2. Xie, X., Wu, D., Xie, M., & Li, Z. (2024). GhostFormer: Efficiently amalgamated CNN-transformer architecture for object detection. Pattern Recognition, 148, 110172.
3. Ben Saad, A., Facciolo, G., & Davy, A. (2024). On the Importance of Large Objects in CNN Based Object Detection Algorithms. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 533-542).
4. Haryono, A., Jati, G., & Jatmiko, W. (2024). Oriented object detection in satellite images using convolutional neural network based on ResNeXt. ETRI Journal, 46(2), 307-322.
5. Sagar, A. S., Chen, Y., Xie, Y., & Kim, H. S. (2024). MSA R-CNN: A comprehensive approach to remote sensing object detection and scene understanding. Expert Systems with Applications, 241, 122788.
6. Amjoud, A. B., & Amrouch, M. (2023). Object detection using deep learning, CNNs and vision transformers: A review. IEEE Access, 11, 35479-35516.
7. Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., ... & Luo, P. (2023). Sparse r-cnn: An end-to-end framework for object detection. IEEE transactions on pattern analysis and machine intelligence.
8. Ye, T., Qin, W., Zhao, Z., Gao, X., Deng, X., & Ouyang, Y. (2023). Real-time object detection network in UAV-vision based on CNN and transformer. IEEE Transactions on Instrumentation and Measurement, 72, 1-13.
9. Vaishnavi, K., Reddy, G. P., Reddy, T. B., Iyengar, N., & Shaik, S. (2023). Real-time object detection using deep learning. Journal of Advances in Mathematics and Computer Science, 38(8), 24-32.
10. Li, W. Z., Zhou, J. W., Li, X., Cao, Y., & Jin, G. (2023). Few-shot object detection on aerial imagery via deep metric learning and knowledge inheritance. International Journal of Applied Earth Observation and Geoinformation, 122, 103397.
11. Demirel, B., Baran, O. B., & Cinbis, R. G. (2023). Meta-tuning loss functions and data augmentation for few-shot object detection. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7339-7349).
12. Han, J., Ren, Y., Ding, J., Yan, K., & Xia, G. S. (2023, June). Few-shot object detection via variational feature aggregation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 1, pp. 755-763).
13. Wang, Y., Zou, X., Yan, L., Zhong, S., & Zhou, J. (2024). SNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12544-12553).
14. Fan, Q., Zhuo, W., Tang, C. K., & Tai, Y. W. (2024). FSODv2: A Deep Calibrated Few-Shot Object Detection Network. International Journal of Computer Vision, 1-20.
15. Shangguan, Z., & Rostami, M. (2023). Identification of novel classes for improving few-shot object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3356-3366).
16. Xin, Z., Wu, T., Chen, S., Zou, Y., Shao, L., & You, X. (2024). Ecea: Extensible co-existing attention for few-shot object detection. IEEE Transactions on Image Processing.
17. Noel, G. P. (2024). Evaluating AI‐powered text‐to‐image generators for anatomical illustration: a comparative study. Anatomical Sciences Education, 17(5), 979-983.
18. Horvath, A. S., & Pouliou, P. (2024). AI for conceptual architecture: Reflections on designing with text-to-text, text-to-image, and image-to-image generators. Frontiers of Architectural Research, 13(3), 593-612.
19. Xue, Z., Song, G., Guo, Q., Liu, B., Zong, Z., Liu, Y., & Luo, P. (2024). Raphael: Text-to-image generation via large mixture of diffusion paths. Advances in Neural Information Processing Systems, 36.
20. Khachatryan, L., Movsisyan, A., Tadevosyan, V., Henschel, R., Wang, Z., Navasardyan, S., & Shi, H. (2023). Text2video-zero: Text-to-image diffusion models are zero-shot video generators. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 15954-15964).
21. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
22. Reith, T.P.; D’Alessandro, D.M.; D’Alessandro, M.P. Capability of multimodal large language models to interpret pediatric radiological images. Pediatr. Radiol. 2024, 54, 1729–1737.
23. Martin, S.A.; Zhao, A.; Qu, J.; Imms, P.E.; Irimia, A.; Barkhof, F.; Cole, J.H.; Initiative, A.D.N. Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosis. medRxiv 2025.
24. Mishra, S. R., & Dash, S. (2026). AI‐Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper‐Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
25. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211-224). Cham: Springer Nature Switzerland.
26. Xue, C.; Kowshik, S.S.; Lteif, D.; Puducheri, S.; Jasodanand, V.H.; Zhou, O.T.; Walia, A.S.; Guney, O.B.; Zhang, J.D.; Pham, S.T.; et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. 2024, 30, 2977–2989.
27. Schilcher, J.; Nilsson, A.; Andlid, O.; Eklund, A. Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures. Comput. Biol. Med. 2024, 168, 107704.
28. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
29. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
30. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
31. Niu, S.; Ma, J.; Bai, L.; Wang, Z.; Guo, L.; Yang, X. EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation. Inf. Fusion 2024, 102, 102069.
32. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
33. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-8). IEEE.
34. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317
35. Zeng, L.; Ma, P.; Li, Z.; Liang, S.; Wu, C.; Hong, C.; Li, Y.; Cui, H.; Li, R.; Wang, J.; et al. Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia. Adv. Sci. 2024, 11, 2404047.
36. Khuntuli, B., Dash, S., Pradhan, P. C., & Mishra, S. R. Combating food insecurity through remote sensing and machine learning for enhanced crop yield prediction. In Intelligent Computing Techniques and Applications (pp. 135-140). CRC Press.
37. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
38. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
39. Li, B.; Chen, H.; Lin, X.; Duan, H. Multimodal Learning system integrating electronic medical records and hysteroscopic images for reproductive outcome prediction and risk stratification of endometrial injury: A multicenter diagnostic study. Int. J. Surg. 2024, 110, 3237–3248.
40. Zhu, L.; Lai, Y.; Ta, N.; Cheng, L.; Chen, R. Multimodal approach in the diagnosis of urologic malignancies: critical assessment of ChatGPT-4V’s image-reading capabilities. JCO Clin. Cancer Inform. 2024, 8, e2300275.
41. Lin, A.C.; Liu, Z.; Lee, J.; Ranvier, G.F.; Taye, A.; Owen, R.; Matteson, D.S.; Lee, D. Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study. Surgery 2024, 175, 121–127.
42. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.Muzahid, A. A. M., Wanggen, W., Sohel, F., Bennamoun, M., Hou, L., & Ullah, H. (2021). Progressive conditional GAN-based augmentation for 3D object recognition. Neurocomputing, 460, 20-30.
43. Kumpatla, G., Veresi, H., Abhishek, S., & Anjali, T. (2023, October). Revolutionizing brain tumour prediction: A pioneering gan-based framework for synthetic data generation. In 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 548-553). IEEE.
2. Xie, X., Wu, D., Xie, M., & Li, Z. (2024). GhostFormer: Efficiently amalgamated CNN-transformer architecture for object detection. Pattern Recognition, 148, 110172.
3. Ben Saad, A., Facciolo, G., & Davy, A. (2024). On the Importance of Large Objects in CNN Based Object Detection Algorithms. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 533-542).
4. Haryono, A., Jati, G., & Jatmiko, W. (2024). Oriented object detection in satellite images using convolutional neural network based on ResNeXt. ETRI Journal, 46(2), 307-322.
5. Sagar, A. S., Chen, Y., Xie, Y., & Kim, H. S. (2024). MSA R-CNN: A comprehensive approach to remote sensing object detection and scene understanding. Expert Systems with Applications, 241, 122788.
6. Amjoud, A. B., & Amrouch, M. (2023). Object detection using deep learning, CNNs and vision transformers: A review. IEEE Access, 11, 35479-35516.
7. Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., ... & Luo, P. (2023). Sparse r-cnn: An end-to-end framework for object detection. IEEE transactions on pattern analysis and machine intelligence.
8. Ye, T., Qin, W., Zhao, Z., Gao, X., Deng, X., & Ouyang, Y. (2023). Real-time object detection network in UAV-vision based on CNN and transformer. IEEE Transactions on Instrumentation and Measurement, 72, 1-13.
9. Vaishnavi, K., Reddy, G. P., Reddy, T. B., Iyengar, N., & Shaik, S. (2023). Real-time object detection using deep learning. Journal of Advances in Mathematics and Computer Science, 38(8), 24-32.
10. Li, W. Z., Zhou, J. W., Li, X., Cao, Y., & Jin, G. (2023). Few-shot object detection on aerial imagery via deep metric learning and knowledge inheritance. International Journal of Applied Earth Observation and Geoinformation, 122, 103397.
11. Demirel, B., Baran, O. B., & Cinbis, R. G. (2023). Meta-tuning loss functions and data augmentation for few-shot object detection. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7339-7349).
12. Han, J., Ren, Y., Ding, J., Yan, K., & Xia, G. S. (2023, June). Few-shot object detection via variational feature aggregation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 1, pp. 755-763).
13. Wang, Y., Zou, X., Yan, L., Zhong, S., & Zhou, J. (2024). SNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12544-12553).
14. Fan, Q., Zhuo, W., Tang, C. K., & Tai, Y. W. (2024). FSODv2: A Deep Calibrated Few-Shot Object Detection Network. International Journal of Computer Vision, 1-20.
15. Shangguan, Z., & Rostami, M. (2023). Identification of novel classes for improving few-shot object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3356-3366).
16. Xin, Z., Wu, T., Chen, S., Zou, Y., Shao, L., & You, X. (2024). Ecea: Extensible co-existing attention for few-shot object detection. IEEE Transactions on Image Processing.
17. Noel, G. P. (2024). Evaluating AI‐powered text‐to‐image generators for anatomical illustration: a comparative study. Anatomical Sciences Education, 17(5), 979-983.
18. Horvath, A. S., & Pouliou, P. (2024). AI for conceptual architecture: Reflections on designing with text-to-text, text-to-image, and image-to-image generators. Frontiers of Architectural Research, 13(3), 593-612.
19. Xue, Z., Song, G., Guo, Q., Liu, B., Zong, Z., Liu, Y., & Luo, P. (2024). Raphael: Text-to-image generation via large mixture of diffusion paths. Advances in Neural Information Processing Systems, 36.
20. Khachatryan, L., Movsisyan, A., Tadevosyan, V., Henschel, R., Wang, Z., Navasardyan, S., & Shi, H. (2023). Text2video-zero: Text-to-image diffusion models are zero-shot video generators. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 15954-15964).
21. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
22. Reith, T.P.; D’Alessandro, D.M.; D’Alessandro, M.P. Capability of multimodal large language models to interpret pediatric radiological images. Pediatr. Radiol. 2024, 54, 1729–1737.
23. Martin, S.A.; Zhao, A.; Qu, J.; Imms, P.E.; Irimia, A.; Barkhof, F.; Cole, J.H.; Initiative, A.D.N. Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosis. medRxiv 2025.
24. Mishra, S. R., & Dash, S. (2026). AI‐Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper‐Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
25. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211-224). Cham: Springer Nature Switzerland.
26. Xue, C.; Kowshik, S.S.; Lteif, D.; Puducheri, S.; Jasodanand, V.H.; Zhou, O.T.; Walia, A.S.; Guney, O.B.; Zhang, J.D.; Pham, S.T.; et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. 2024, 30, 2977–2989.
27. Schilcher, J.; Nilsson, A.; Andlid, O.; Eklund, A. Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures. Comput. Biol. Med. 2024, 168, 107704.
28. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
29. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
30. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
31. Niu, S.; Ma, J.; Bai, L.; Wang, Z.; Guo, L.; Yang, X. EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation. Inf. Fusion 2024, 102, 102069.
32. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
33. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-8). IEEE.
34. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317
35. Zeng, L.; Ma, P.; Li, Z.; Liang, S.; Wu, C.; Hong, C.; Li, Y.; Cui, H.; Li, R.; Wang, J.; et al. Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia. Adv. Sci. 2024, 11, 2404047.
36. Khuntuli, B., Dash, S., Pradhan, P. C., & Mishra, S. R. Combating food insecurity through remote sensing and machine learning for enhanced crop yield prediction. In Intelligent Computing Techniques and Applications (pp. 135-140). CRC Press.
37. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
38. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE.
39. Li, B.; Chen, H.; Lin, X.; Duan, H. Multimodal Learning system integrating electronic medical records and hysteroscopic images for reproductive outcome prediction and risk stratification of endometrial injury: A multicenter diagnostic study. Int. J. Surg. 2024, 110, 3237–3248.
40. Zhu, L.; Lai, Y.; Ta, N.; Cheng, L.; Chen, R. Multimodal approach in the diagnosis of urologic malignancies: critical assessment of ChatGPT-4V’s image-reading capabilities. JCO Clin. Cancer Inform. 2024, 8, e2300275.
41. Lin, A.C.; Liu, Z.; Lee, J.; Ranvier, G.F.; Taye, A.; Owen, R.; Matteson, D.S.; Lee, D. Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study. Surgery 2024, 175, 121–127.
42. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.Muzahid, A. A. M., Wanggen, W., Sohel, F., Bennamoun, M., Hou, L., & Ullah, H. (2021). Progressive conditional GAN-based augmentation for 3D object recognition. Neurocomputing, 460, 20-30.
43. Kumpatla, G., Veresi, H., Abhishek, S., & Anjali, T. (2023, October). Revolutionizing brain tumour prediction: A pioneering gan-based framework for synthetic data generation. In 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 548-553). IEEE.
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