Bearing Target Detection Dataset

In the current industrial landscape, quality control in mechanical assembly lines faces challenges such as misidentification of bearing types and inconsistent inspection results. Existing solutions often rely on manual inspection, which can lead to human errors and inefficiencies. This dataset aims to address the challenge of automated bearing detection and classification, fulfilling the need for reliable quality assurance processes. The data is collected using high-resolution cameras in controlled environments, ensuring optimal lighting and minimal background noise. Quality control measures include multi-round annotations, consistency checks, and expert reviews to guarantee data accuracy. The dataset is organized in JPG format with structured folders for easy access and retrieval. Each image is accompanied by a JSON file detailing annotations and quality scores.