<p>Object detection is an increasingly popular tool used in many fields, especially in the<br>
development of autonomous vehicles. The task of object detections involves the localization<br>
of objects in an image, constructing a bounding box to determine the presence and loca-<br>
tion of the object, and classifying each object into its appropriate class. Object detection<br>
applications are commonly implemented using convolutional neural networks along with the<br>
construction of feature pyramid networks to extract data.<br>
Another commonly used technique in the automotive industry is sensor fusion. Each<br>
automotive sensor – camera, radar, and lidar – have their own advantages and disadvantages.<br>
Fusing two or more sensors together and using the combined information is a popular method<br>
of balancing the strengths and weakness of each independent sensor. Together, using sensor<br>
fusion within an object detection network has been found to be an effective method of<br>
obtaining accurate models. Accurate detections and classifications of images is a vital step<br>
in the development of autonomous vehicles or self-driving cars.<br>
Many studies have proposed methods to improve neural networks or object detection<br>
networks. Some of these techniques involve data augmentation and hyperparameter opti-<br>
mization. This thesis achieves the goal of improving a camera and radar fusion network by<br>
implementing various techniques within these areas. Additionally, a novel idea of integrating<br>
a third sensor, the lidar, into an existing camera and radar fusion network is explored in this<br>
research work.<br>
The models were trained on the Nuscenes dataset, one of the biggest automotive datasets<br>
available today. Using the concepts of augmentation, hyperparameter optimization, sensor<br>
fusion, and annotation filters, the CRF-Net was trained to achieve an accuracy score that<br>
was 69.13% higher than the baseline</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/19638648 |
| Date | 12 July 2022 |
| Creators | Sheetal Prasanna (12447189) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/SENSOR_FUSION_IN_NEURAL_NETWORKS_FOR_OBJECT_DETECTION/19638648 |
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