Garbage Classification Recognition Image Dataset

Currently, with the acceleration of urbanization, waste management has become a major challenge in global environmental protection. Traditional garbage classification methods rely on manual processes, which are inefficient and prone to errors, thus increasing the demand for intelligent garbage classification technology. Existing solutions are mostly based on foreign data, lacking localized adaptability, and tend to be less accurate in complex scenarios. This dataset focuses on optimizing image recognition applications in garbage classification, addressing issues of regional adaptability and recognition accuracy in complex environments. The data is collected by HD cameras in diverse urban environments, including multi-angle and varying lighting conditions, ensuring sample diversity. Quality is ensured through multiple rounds of annotation and consistency checks, along with rigorous reviews by environmental science experts. The annotation team includes 10 professionals with backgrounds in environmental science, and data preprocessing involves denoising and image enhancement techniques to improve recognizability, stored in JPG format, organized by garbage type and shooting conditions. Annotation accuracy exceeds 95%, ensuring high quality. The dataset introduces automated annotation tools to improve efficiency and employs the latest data augmentation technologies to expand sample diversity. It addresses the confusion problem in easily mixed categories in garbage recognition, with experiments showing that using this dataset improves system recognition accuracy by over 20%. Compared to traditional datasets, this integrated localized data has the advantage of broad coverage, suitable for various global environments, and can be expanded to other environment-related tasks.