• Y. Swathi
• K. Stinkens Shailaja Mouna
• G. Tejaswi
• N. Nandini
• T. Sadvika

Hey there! Let's talk about how important object detection is for self-driving cars. It helps them navigate safely and efficiently by recognizing and reacting to what's around them in real-time. This study looks into using the YOLO (You Only Look Once) algorithm, specifically YOLOv4, for spotting objects quickly in vehicles. YOLOv4 was chosen because it strikes a great balance between speed and accuracy, crucial for making fast decisions while driving autonomously. The model was trained on the COCO dataset, which has lots of different object classes, and then tested the KITTI dataset with realistic driving scenarios. They used metrics like precision, recall, F1-score, and mean Average Precision (mAP) to check how well YOLOv4 performed. Turns out it did a great job at detecting pedestrians, vehicles, and traffic signs even in tough situations like low light or when things are partially hidden. They also talked about how they got the data ready, trained the model, and made sure it can work in real-time. But even though YOLOv4 did really well, there are still some areas where it struggles, like finding small or hidden objects and handling all that math needed for quick processing. In a nutshell, this research shows that YOLOv4 can boost safety in self-driving cars by spotting objects effectively. It also points out ways to make detection algorithms better for the future, such as improving the model design and combining data from different sensors.

Object detection navigation Interference Optimization

"OBJECT DETECTION USING YOLO", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.343-357, October-2024, Available :http://www.jetir.org/papers/JETIRGN06039.pdf

"OBJECT DETECTION USING YOLO", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp343-357, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06039.pdf