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Exploring the viability of bypassing the image signal processor for CNN-based object detection in autonomous vehicles

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posted on 2023-05-29, 08:39 authored by Jordan Cahill, Ashkan Parsi, Darragh Mullins, Jonathan Horgan, Enda Ward, Ciarán EisingCiarán Eising, Patrick DennyPatrick Denny, Brian Deegan, Martin Glavin, Edward Jones

In the field of autonomous driving, cameras are crucial sensors for providing information about a vehicle’s environment. Image quality refers to a camera system’s ability to capture, process, and display signals to form an image. Historically, ‘‘good quality’’ in this context refers to images that have been processed by an Image Signal Processor (ISP) designed with the goal of providing the optimal experience for human consumption. However, image quality perceived by humans may not always result in optimal conditions for computer vision. In the context of human consumption, image quality is well documented and understood. Image quality for computer vision applications, such as those in the autonomous vehicle industry, requires more research. Fully autonomous vehicles inevitably encounter constraints concerning data storage, transmission speed, and energy consumption. This is a result of enormous amounts of data being generated by the vehicle from suites made up of multiple different sensors. We propose a potential optimization along the computer vision pipeline, by completely bypassing the ISP block for a class of applications. We demonstrate that doing so has a negligible impact on the performance of Convolutional Neural Network (CNN) object detectors. The results also highlight the benefits of using raw pre-ISP data, in the context of computation and energy savings achieved by removing the ISP.

Funding

Lero - the Irish Software Research Centre

Science Foundation Ireland

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Publication

IEEE Access, 2023, vol. 11, pp. 42302-42313

Publisher

IEEE Computer Society

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