Corridor Human Detection Energy-Saving Lighting Installation Design Dataset

In the field of smart devices, corridor lighting systems face issues of high energy consumption and inefficiency. Existing lighting solutions often lack real-time human traffic detection functionality, resulting in overuse of electricity and resource waste. This dataset aims to provide high-quality human detection image data to support the design of intelligent lighting systems and energy-saving optimization. Through high-precision human recognition, the lighting intensity can be adjusted in time to reduce power consumption. The data is collected with high-resolution cameras in various corridor environments, combined with multiple rounds of annotation and expert review to ensure high-quality labeling. The team is composed of computer vision experts, with a scale of over 30 people. Data is preprocessed with noise reduction and normalization, and stored in JPG format to ensure quick retrieval and use. This dataset is renowned for its labeling accuracy of over 95% and completeness. Employing innovative label smoothing technology and multi-angle data augmentation, it enhances model generalization capabilities. Applied in intelligent lighting systems, it can reduce energy consumption by up to 30%. Compared to similar market data, it has significant advantages in diversity and data volume, including unique long corridor scene data, and supports cross-industry application expansion.