This paper presents a comprehensive survey of recent advances in object detection for optical remote sensing images and introduces a new benchmark dataset named DIOR. The DIOR dataset contains 23,463 images and 192,472 object instances across 20 object classes. It is characterized by large-scale data, wide object size variations, rich image variations, and high inter-class similarity with intra-class diversity. The dataset is publicly available and designed to address the limitations of existing datasets in terms of image diversity, object category scale, and variations in object size and appearance. The paper evaluates several state-of-the-art object detection methods on the DIOR dataset to establish a baseline for future research. The DIOR dataset is intended to help researchers develop and validate data-driven methods for object detection in optical remote sensing images. The paper also reviews recent progress in object detection methods in both the computer vision and earth observation communities, highlighting the challenges and differences between natural scene images and remote sensing images. The DIOR dataset is designed to overcome the limitations of existing datasets and provide a more comprehensive and diverse benchmark for object detection in optical remote sensing images.This paper presents a comprehensive survey of recent advances in object detection for optical remote sensing images and introduces a new benchmark dataset named DIOR. The DIOR dataset contains 23,463 images and 192,472 object instances across 20 object classes. It is characterized by large-scale data, wide object size variations, rich image variations, and high inter-class similarity with intra-class diversity. The dataset is publicly available and designed to address the limitations of existing datasets in terms of image diversity, object category scale, and variations in object size and appearance. The paper evaluates several state-of-the-art object detection methods on the DIOR dataset to establish a baseline for future research. The DIOR dataset is intended to help researchers develop and validate data-driven methods for object detection in optical remote sensing images. The paper also reviews recent progress in object detection methods in both the computer vision and earth observation communities, highlighting the challenges and differences between natural scene images and remote sensing images. The DIOR dataset is designed to overcome the limitations of existing datasets and provide a more comprehensive and diverse benchmark for object detection in optical remote sensing images.