Ensemble of Fast R-CNN with Bi-LSTM for Object Detection
R Sasirekha, V Surya, P. Nandhini, Preethy Jemima P, T Bhanushree, G Hanitha · 2025
Object detection is a critical task in computer vision, with applications ranging from autonomous driving to medical imaging. Traditional object detection models, such as Fast R-CNN, have shown remarkable performance by leveraging Convolutional Neural Networks (CNNs) for feature extraction and region proposal generation. However, these models often face challenges in scenarios where contextual understanding or the relationship between multiple objects in an image plays a key role in accurate detection. To address these limitations, we propose an ensemble framework that combines Fast R-CNN with a Bidirectional Long Short-Term Memory (Bi-LSTM) network for improved object detection performance. The proposed ensemble leverages the strengths of both Fast R-CNN and Bi-LSTM in a complementary manner. Experimental results demonstrate that the ensemble of Fast R-CNN with Bi-LSTM outperforms traditional object detection methods on standard benchmark datasets, offering improvements in both detection accuracy and localization precision. This framework is particularly beneficial in real-world applications such as autonomous navigation and surveillance, where contextual understanding and spatial reasoning are essential for reliable object detection.