Power Socket Copper Plate Installation Detection Dataset

In the industrial sector, the quality of power socket assembly is critical to ensure safety and functionality. However, there are significant challenges such as inconsistent installation and human error, leading to potential failures in electrical systems. Existing solutions often rely on manual inspection, which can be time-consuming and error-prone. This dataset aims to address these issues by providing a comprehensive resource for training machine learning models that can automatically detect whether conductive components are installed correctly, thereby improving efficiency and reliability. The dataset consists of 5000 images captured under controlled lighting conditions using high-resolution cameras, focusing on various angles of power sockets. Quality control measures include multi-round annotation, consistency checks among annotators, and expert reviews to ensure high annotation precision. The data is stored in JPG format, organized in a directory structure that allows for easy access and retrieval of images based on their installation status.