Object Detection and Predictive Maintenance in Autonomous Cars in YOLO, A Survey

This study deeply examines the object detection techniques in autonomous vehicles. Two requirements should be met by autonomous vehicles’ object detection algorithms: First, a high level of accuracy is required. Second is real-time detecting speed. As autonomous vehicle’s core is object detection, which enables self-driving cars to precisely sense their environment and react appropriately to objects they detect. But in practical settings, developing a reliable and extremely precise system still presents significant difficulties because of restrictions such fluctuating ambient conditions, sensor limitations, and computational resource constraints. Degradation of the sensor in bad weather or low light, for instance, can significantly reduce the accuracy of detection. Considering this we have proposed to in corporate predictive maintenance in object detection. We also highlighted the performance metrics used in the proposed framework. Further a detailed literature review is included regarding object detection in autonomous vehicles specially in adverse weather condition following with analysis on the current work.