Max Olli Blom and Thomas Johansen, MPCAS

Title of thesis:
 End-to-End Object Detection on Raw Sensor Data

Follow the presentation online
Passcode: 091717​


In recent years, convolutional neural networks have outperformed previous state-of the-art methods on several computer vision tasks. However, prior to the network training, there are in general several non-trainable image processing steps that map the raw sensor data to the input image, and even though each of these sequential steps can be individually hand-tuned to maximize the perceptual image quality, since they are non-trainable, they are not optimized for computer vision nor the end task of the network. Our work investigates the possibility of removing and/or replacing traditional image signal processing operations using a deep learning approach with the goal of increasing performance and reducing the runtime of an object detection system. We show that the use of raw camera sensor data together with end-to-end trainable image processing yields an increase in performance, even with very few operations. Our results show the potential of this approach, which easily generalizes to other areas of computer vision. ​
Supervisor: Christoffer Petersson and Adam Tonderski (Zenseact)
Examiner: Mats Granath
Opponents: Silas Ulander and Erik Brorsson
Category Student project presentation
Location: Online via Zoom
Starts: 26 January, 2021, 10:00
Ends: 26 January, 2021, 11:00

Page manager Published: Wed 20 Jan 2021.