A Hybrid Deep Learning Framework for Robust Object Detection in Smart Industrial and Medical Imaging Applications
DOI:
https://doi.org/10.25139/ijair.v7i2.10576Keywords:
Cross-Domain Learning, Deep Learning, Industrial Automation, Medical Imaging, Object DetectionAbstract
The convergence of deep learning and real-time object detection has enabled significant advancements in both industrial automation and medical diagnostics. However, a persistent challenge lies in the fragmentation between 2D and 3D object detection models, which limits scalability and cross-domain applicability. This study proposes a hybrid object detection framework that integrates convolutional features with classical template matching, aiming to improve detection robustness, especially in cluttered and occluded scenes. The proposed method is evaluated using standard 2D datasets and occlusion-heavy custom scenarios, showing improved performance in terms of precision (89.2%), recall (87.5%), and [email protected] (85.6%) compared to Faster R-CNN and YOLOv3. It achieves real-time inference speeds of 18.9 FPS (1080p) and 29.4 FPS (720p). While the paper discusses potential applications for industrial and medical domains, evaluations using LASIESTA, KITTI, or CT-scan datasets are presented as conceptual use cases rather than direct experimental results. The findings suggest the framework is promising for deployment in safety-critical environments such as robotic inspection systems and diagnostic imaging workflows.
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