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Micro gears are often used in precise instruments and devices, and the error of their tooth length is closely related to the precision of an entire instrument. Traditional gear detection is mainly performed manually,however, low precision and inaccurate quantification exist in manual detection. To accurately calculate the tooth length error and obtain quantified results, this paper proposed a system to detect the tooth length error of micro gears based on machine vision. In this system, firstly, the image noise was removed by wavelet transform; then Radon transform method was adopted to correct the part image; a cubic curve model based on local characteristics was employed to extract the sub-pixel edge information of an interested area, and the boundary location was accurately calculated through projection mapping; finally, the dynamic range of the gear center was calculated to judge the tooth length. The experimental results show that this method can reach a precision up to 2 μm, an accuracy rate up to 99%, and an average single-frame detection time of 18 ms, as well as obtain reliable conclusions of a part about 5s. With efficiency and accuracy, this method can meet the requirements of industrial detection.