Obstacle Detection Using Monocular Camera with Mask R-CNN Method

Abstract

An autonomous car is a car that can operate without being controlled by humans. Autonomous cars must be able to detect obstacles so that the car does not hit objects that are on the path to be traversed. Therefore, it takes a variety of sensors to determine the surrounding conditions. The sensors commonly used in autonomous cars are cameras and LiDAR. Compared to LiDAR, the camera has a relatively long detection distance, lower cost, and can be used to classify objects. In this final project, the monocular camera and Mask R-CNN algorithm are used to create a system that can detect obstacles in the form of cars, motorcycles, and humans. The system will generate segmentation instances, bounding boxes, classifications, distance, and width estimation for each detected object. By using a custom dataset that is created manually it fits perfectly with the surrounding environment. The system used can produce a Mean Average Precision of 0.81, a Mean Average Recall of 0.89, an F1 score of 0.86, and a Mean Absolute Percentage Error of 13.4% for the distance estimator. The average detection speed of each image is 0.29 seconds.