Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms. In this paper, we present preliminary results using only camera images for detecting various objects using deep learning network, as a first step toward multi-sensor fusion algorithm development. The simulation experiments based on camera images show encouraging results where the proposed deep learning network based detection algorithm was able to detect various objects with certain degree of confidence. A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time

3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data

Traffic volume data collection is a crucial aspect of transportation engineering and urban planning, as it provides vital insights into traffic patterns, congestion, and infrastructure efficiency. Traditional manual methods of traffic data collection are both time-consuming and costly. However, the emergence of modern technologies, particularly Light Detection and Ranging (LiDAR), has revolutionized the process by enabling efficient and accurate data collection. Despite the benefits of using LiDAR for traffic data collection, previous studies have identified two major limitations that have impeded its widespread adoption. These are the need for multiple LiDAR systems to obtain complete point cloud information of objects of interest, as well as the labor-intensive process of annotating 3D bounding boxes for object detection tasks. In response to these challenges, the current study proposes an innovative framework that alleviates the need for multiple LiDAR systems and simplifies the laborious 3D annotation process. To achieve this goal, the study employed a single LiDAR system, that aims at reducing the data acquisition cost and addressed its accompanying limitation of missing point cloud information by developing a Point Cloud Completion (PCC) framework to fill in missing point cloud information using point density. Furthermore, we also used zero-shot learning techniques to detect vehicles and pedestrians, as well as proposed a unique framework for extracting low to high features from the object of interest, such as height, acceleration, and speed. Using the 2D bounding box detection and extracted height information, this study is able to generate 3D bounding boxes automatically without human intervention.Comment: 19 pages, 11 figures. This paper has been submitted for consideration for presentation at the 103rd Annual Meeting of the Transportation Research Board, January 202

Development of Detection and Tracking Systems for Autonomous Vehicle using Machine Learning

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Tyler Ward on April 25, 2023

Using Prior Knowledge for Verification and Elimination of Stationary and Variable Objects in Real-time Images

With the evolving technologies in the autonomous vehicle industry, now it has become possible for automobile passengers to sit relaxed instead of driving the car. Technologies like object detection, object identification, and image segmentation have enabled an autonomous car to identify and detect an object on the road in order to drive safely. While an autonomous car drives by itself on the road, the types of objects surrounding the car can be dynamic (e.g., cars and pedestrians), stationary (e.g., buildings and benches), and variable (e.g., trees) depending on if the location or shape of an object changes or not. Different from the existing image-based approaches to detect and recognize objects in the scene, in this research 3D virtual world is employed to verify and eliminate stationary and variable objects to allow the autonomous car to focus on dynamic objects that may cause danger to its driving. This methodology takes advantage of prior knowledge of stationary and variable objects presented in a virtual city and verifies their existence in a real-time scene by matching keypoints between the virtual and real objects. In case of a stationary or variable object that does not exist in the virtual world due to incomplete pre-existing information, this method uses machine learning for object detection. Verified objects are then removed from the real-time image with a combined algorithm using contour detection and class activation map (CAM), which helps to enhance the efficiency and accuracy when recognizing moving objects

What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics

This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the places through which a robot moves. We leverage the concept of 'experiences' in visual perception for robotics, accounting for bias in the data a robot sees by fitting object detector models to a particular place. The key question we seek to answer in this paper is simply: how do we define a place? We build bespoke pedestrian detector models for autonomous driving, highlighting the necessary trade off between generalisation and model capacity as we vary the extent of the place we fit to. We demonstrate a sizeable performance gain over a current state-of-the-art detector when using computationally lightweight bespoke place-fitted detector models.Comment: IROS 201