Optimizing Convolutional Neural Networks for Real-Time Object Detection in Autonomous Vehicles

This study explores the optimization and reliability of convolutional neural network (CNN) models, namely YOLOv5, for real-time object detection in autonomous vehicles under varying environmental conditions. The study intends to evaluate the accuracy and speed of baseline models and optimized models based on the pruning, quantization, and knowledge distillation methods. With public datasets (KITTI, nuScenes, COCO) and local datasets from Islamabad and Karachi, the models were fine-tuned and trained to condition themselves to drive according to local driving conditions. Quantitatively, performance metrics such as mean Average Precision (mAP), frames per second (FPS), model size, and energy use were evaluated on high-end GPUs and embedded systems such as NVIDIA Jetson Xavier. Statistical tests such as paired t-tests and repeated measures ANOVA indicated that pruning decreases model size at the expense of slightly lower accuracy, quantization significantly accelerates inference while preserving good accuracy, and knowledge distillation achieves the optimal trade-off by retaining high accuracy and stability under harsh conditions such as low light, rain, and occlusion. These results emphasize the most essential trade-offs between efficiency and reliability in the deployment of deep models for autonomous vehicle perception systems. The research proposes using knowledge distillation via multi-condition learning and hardware-aware optimization to design reliable, real-time sufficient object detectors. This work establishes the practical deployment of efficient and reliable CNN models in autonomous vehicles for improved real-world performance and safety.