Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection

Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Molloy, Dara, Muller, Patrick, Deegan, Brian, Mullins, Darragh, Horgan, Jonathan, Ward, Enda, Jones, Edward, Braun, Alexander, Glavin, Martin
Format: Article
Language:English
Subjects:
ADAS
Advanced driver assistance systems
Algorithms
Autonomous vehicles
Cameras
Computer vision
Datasets
Deep learning
Degradation
Frequency response
Impact analysis
Intelligent Transportation System
Intelligent transportation systems
Lens Blur
Lenses
Manufacturing
Object detection
Object recognition
Pedestrians
Safety critical
Sensors
Spatial resolution
Telematics
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Summary:Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3348663