Object Detection Based on Raw Bayer Images

Bayer pattern is a widely used Color Filter Array (CFA) for digital image sensors, efficiently capturing different light wavelengths on different pixels without the need for a costly ISP pipeline. The resulting single-channel raw Bayer images offer benefits such as spectral wavelength sensitivity and low time latency. However, object detection based on Bayer images has been underexplored due to challenges in human observation and algorithm design caused by the discontinuous color channels in adjacent pixels. To address this issue, we propose the BayerDetect network, an end-to-end deep object detection framework that aims to achieve fast, accurate, and memory-efficient object detection. Unlike RGB color images, where each pixel encodes spectral context from adjacent pixels during ISP color interpolation, raw Bayer images lack spectral context. To enhance the spectral context, the BayerDetect network introduces a spectral frequency attention block, transforming the raw Bayer image pattern to the frequency domain. In object detection, clear object boundaries are essential for accurate bounding box predictions. To handle the challenges posed by alternating spectral channels and mitigate the influence of discontinuous boundaries, the BayerDetect network incorporates a spatial attention scheme that utilizes deformable convolutional kernels in multiple scales to explore spatial context effectively. The extracted convolutional features are then passed through a sparse set of proposal boxes for detection and classification. We conducted experiments on both public and self-collected raw Bayer images, and the results demonstrate the superb performance of the BayerDetect network in object detection tasks.