不同于人类视觉能够适应各种灯光变化环境，现有的二维人体目标检测算法在剧烈灯光变化场景中其检测性能会明显下降。针对这一问题，本文提出了一种灯光剧烈变化环境自适应的二维人体目标检测方法。首先，基于具有剧烈灯光变化的舞台演出环境，本文采集并构建了一个包含各种灯光颜色和丰富灯光变化的人体图片基准数据集（命名为“StageHuman”），以用于验证当前二维人体目标检测算法的缺陷与不足。其次，提出一种基于风格迁移的数据增强策略，将特定场景图片中的剧烈灯光变化风格迁移到大规模公开数据集COCO的人体图片中，再利用风格迁移后的大规模数据集来训练深度神经网络模型，从而提升模型在剧烈灯光变化环境下的二维人体检测性能。最后，通过大量的实验对比与分析，验证了本文方法能够有效提升深度神经网络模型在剧烈灯光变化环境下的鲁棒性和检测精度，并且该有效性不依赖于具体的风格迁移算法，而主要取决于所迁移的灯光变化风格的多样性和完整性。

Unlike human vision, which can adapt to various lighting environments, the performance of existing 2D human object detection algorithms will be significantly reduced in the scene of drastic lighting changes. In order to solve this problem, in this paper a two-dimensional human object detection method was proposed, which adapted to the environment of drastic lighting changes. Firstly, based on the stage performance environment with drastic lighting changes, a human body image benchmark dataset (named “StageHuman”) containing various light colors and rich light changes was collected and constructed to verify the defects and deficiencies of the current two-dimensional human object detection algorithm. Secondly, a data enhancement strategy based on style transfer was proposed, which migrated the dramatic lighting change style in a specific scene image to the body picture of a large-scale open data set COCO, and then used the large-scale data set after style transfer to train the deep neural network model, so as to improve the two-dimensional human detection performance of the model under the environment of dramatic lighting change. Finally, through a large number of experimental comparison and analysis, it is verified that the proposed method can effectively improve the robustness and detection accuracy of the deep neural network model under the environment of drastic lighting changes, and the effectiveness does not depend on the specific style transfer algorithm, but mainly depends on the diversity and integrity of the lighting change styles transferred.