In many applications, including autonomous driving, surveillance, and augmented reality, where accuracy and speed are critical, real-time object detection is vital. The trade-off between speed and accuracy in real-time object detection using neural networks for image and video recognition tasks is examined in this research. We examine the performance of cutting-edge deep learning models in real-time processing, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), and You Only Look Once (YOLO) architectures. In order to achieve a balance between computational efficiency and detection precision, the study compares a number of optimization techniques, including model pruning, quantization, and knowledge distillation. Furthermore, taking into account practical limits such as processor power and hardware limitations, we examine how various topologies affect detection speed and accuracy by combining both the architectures together to overcome the drawback of speed and accuracy.
Neural networks; Speed vs accuracy; Convolutional neural networks; You Only Look Once; Computational efficiency.
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