Enhancing Autonomous Vehicle Navigation with Real-Time Object Detection Using Convolutional Neural Networks

Autonomous vehicles’ (AVs) capacity to identify and categorise things in real-time is crucial to their development for the sake of efficient and safe navigation. Because of its exceptional capability to learn spatial feature hierarchies automatically, Convolutional Neural Networks (CNNs) have become an indispensable tool for object identification in intricate visual recognition problems. This research offers a thorough analysis of convolutional neural networks (CNNs) used for autonomous vehicle object identification in real-time, with an emphasis on improving detection accuracy and speed. We compare the processing speed, detection accuracy, and computational efficiency of several CNN designs, such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN. In addition, to address the demanding realtime needs of autonomous driving systems, we suggest CNN design improvements that use hardware acceleration methods like GPU and TPU optimisations. The experimental findings show that our suggested method significantly reduces detection latency while maintaining accuracy, which makes it suitable for use in real-world AV settings. Autonomous vehicle technology advances thanks to this study’s results, which aid in building scalable and reliable object identification algorithms.