Lightweight RAW Object Detection for Automated Driving

Modern sensor and image acquisition technologies have enabled Automated Vehicles (AVs) to leverage image data for enhanced on-board visual perception. To achieve this, AVs deploy Computer Vision (CV) deep learning tools for safe and accurate object detection. These systems typically rely on Image Signal Processing (ISP) pipelines to convert RAW sensor data into human-perceivable RGB images, as state-of-the-art deep learning models are optimised for such inputs. However, in safety-critical real-time applications such as AVs, CV systems must not only be reliable and accurate but also fast. In this work, we demonstrate that a lightweight ISP comprising simple processing steps can effectively operate on RAW image data while maintaining strong object detection performance across diverse scenes. Our experiments show that our proposed simple ISP maintains a balanced trade-off between accuracy and latency.