Object Detection for Autonomous Vehicles with Sensor-based Technology Using YOLO

Abstract

The year 2020 has been a tough year with the global pandemic situation, and the utmost priority is to live in a clean, green, and safe environment. One of the areas that the governments are emphasizing for the readiness of our ecosystem is autonomous and contactless environments in adapting to the new norm. Thus, Autonomous Vehicle (AV) is a promising technology to bring forward. One of the critical aspects of Autonomous Navigation is object detection. Most AV use multiple sensors to detect objects, such as cameras, radar and Light Detection and Ranging sensor (LiDAR). Nowadays, the LiDAR sensor is widely implemented due to the ability to detect objects in the form of pulsed lasers, benefiting in low-light object detection. However, even with advanced technology, poor programming can affect the performance of object detection system. Thus, the study explores the state-of-the-art of You Only Look Once (YOLO) algorithms namely Tiny-YOLO and Complex-YOLO for object detection on KITTI dataset. Their performances were compared based on accuracy, precision, and recall metrics. The results showed that the Complex-YOLO has better performance as the mean average precision is higher than the Tiny-YOLO model when tested with equal parameters.