Enhanced Object Detection by Integrating Camera Parameters into Raw Image-Based Faster R-CNN

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.