Object Detection Framework Using Multiple Tone Mappings on High-Dynamic-Range Images

In practical computer vision applications, such as autonomous driving, the ability to effectively process high-dynamic-range (HDR) scenes is crucial for safe operation. In this paper, we focus on object detection within HDR images. To address this, we propose a simple yet effective framework that employs multiple tone mappings. First, we generate multiple images from an HDR image with varying tone mapping parameters. Then, those images are fed into a high-performance object detector pre-trained with low-dynamic-range (LDR) images. Multiple detection results are merged with non-maximum suppression (NMS). To assess the performance of our method, we have built a validation dataset comprising HDR images captured in outdoor scenes with significant contrast variations. The experimental results using both our dataset and an existing one demonstrate that our method outperforms existing approaches<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>The code and the dataset can be available at https://open-vision.sc.e.titech.ac.jp/research/hdrdet.