Although the Region-based Convolutional Neural Network (R-CNN) model effectively utilizes pretrained Convolutional Neural Networks (CNNs) to extract features, it suffers from significant computational inefficiency. Because the model relies on extracting thousands of region proposals from a single input image, it must execute thousands of separate CNN forward propagations to perform object detection. This massive computational burden makes the standard R-CNN architecture too slow and infeasible for widespread use in real-world applications.