Optimizing YOLOv9 for Road Object Detection in Self-Driving Cars Through Hyperparameter Tuning and Advanced Regularization

As a crucial part of autonomous driving, object detection needs to be of high accuracy and real-time. This paper proposes an optimized version of YOLOv9 specifically for road object detection by fine-tuning hyperparameter optimization techniques. Noteworthy is your incorporation of learning rate scheduling strategies-like cosine annealing-to find the best learning rate schedule for your training data; adaptive batch size experimentation in attempts to maximize the performance while utilizing the system's memory; progressive discoveries of the strongest optimizers, such as AdamW and Ranger, and of the strongest regularization methods, dropout, label smoothing, and weight decay, to minimize overfitting; etc. These improvements were tested on the R&S_5K dataset and resulted in considerable gains compared with both versions of YOLOv9. The more complex model scored 0.750 for precision, 0.600 for recall and 0.660 for the F1 score/mAP@0. 5 of 0.670, and mAP@0. 5:0.950 of 0.420 on the key metrics, a performance gain of up to 7 %. The results in the following demonstrate that the proposed optimizations performed effectively to achieve a combination of high accuracy and speedy inference, providing a solid basis for real-time object detection for autonomous driving applications.