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Enhanced object detection by integrating camera parameters into raw image-based faster R-CNN
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Wei, Chuheng, Wu, Guoyuan, Barth, Matthew, Chan, Pak Hung, Donzella, Valentina and Huggett, Anthony (2023) Enhanced object detection by integrating camera parameters into raw image-based faster R-CNN. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)., Bilbao, Bizkaia, Spain, 24-28 Sep 2023 (In Press)
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WRAP-enhanced-object-detection-integrating-camera-parameters-raw-image-based-faster-R-CNN-2023.pdf - Accepted Version - Requires a PDF viewer. Download (3251Kb) | Preview |
Abstract
The rapid progress in intelligent vehicle technology has led to a significant reliance on computer vision and deep neural networks (DNNs) to improve road safety and driving experience. However, the image signal processing (ISP) steps required for these networks, including demosaicing, color correction, and noise reduction, increase the overall processing time and computational resources. To address this, our paper proposes an improved version of the Faster R-CNN algorithm that integrates camera parameters into raw image input, reducing dependence on complex ISP steps while enhancing object detection accuracy. Specifically, we introduce additional camera parameters, such as ISO speed rating, exposure time, focal length, and F-number, through a custom layer into the neural network. Further, we modify the traditional Faster R-CNN model by adding a new fully connected layer, combining these parameters with the original feature maps from the backbone network. Our proposed new model, which incorporates camera parameters, has a 4.2% improvement in mAP@[0.5,0.95] compared to the traditional Faster RCNN model for object detection tasks on raw image data.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Library of Congress Subject Headings (LCSH): | Intelligent transportation systems, Computer vision , Pattern recognition systems, Neural networks (Computer science) , Image processing , Signal processing -- Digital techniques | ||||||
Publisher: | IEEE | ||||||
Official Date: | 2023 | ||||||
Dates: |
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Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Reuse Statement (publisher, data, author rights): | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 1 August 2023 | ||||||
Date of first compliant Open Access: | 2 August 2023 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Bilbao, Bizkaia, Spain | ||||||
Date(s) of Event: | 24-28 Sep 2023 | ||||||
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