Enhancing Precision Object Detection and Identification for Autonomous Vehicles through YOLOv5 Refinement with YOLO-ALPHA

Advancing swiftly in contemporary society, the rapid growth of autonomous driving technology suggests its potential adoption across continents.The realization of fully autonomous driving relies on proficiently detecting, classifying, and tracking road objects such as pedestrians and vehicles.This research employs the YOLOv5 neural network, enhancing it with YOLO-ALPHA.Modifications, encompassing freeze and attention mechanisms, serve to refine accuracy and expedite training.Furthermore, adjustments to the activation function aim to stabilize precision and recall.The integration of an FCN based on semantic segmentation theory contributes to improved accuracy in detecting road conditions during autonomous driving.Consequently, this enables the successful and highly accurate functionality of automatic identification.